U.S. patent application number 15/858569 was filed with the patent office on 2019-02-07 for technologies for storage discovery and reallocation.
The applicant listed for this patent is Intel Corporation. Invention is credited to Gamil Cain, Anjaneya Reddy Chagam Reddy, Teddy Greer, Sujoy Sen.
Application Number | 20190042126 15/858569 |
Document ID | / |
Family ID | 65037934 |
Filed Date | 2019-02-07 |
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United States Patent
Application |
20190042126 |
Kind Code |
A1 |
Sen; Sujoy ; et al. |
February 7, 2019 |
TECHNOLOGIES FOR STORAGE DISCOVERY AND REALLOCATION
Abstract
Technologies for storage discovery and reallocation include a
compute device. The compute device is to receive, from a data
storage sled, storage device data from a storage device located on
the data storage sled. The storage device data includes storage
device self-test data that defines a result of a self-test
performed by the storage device. The compute device is also to
determine, in response to the storage device self-test data,
whether the storage device fails to satisfy a performance
threshold. Further, the compute device is to generate, in response
to a determination that the storage device fails to satisfy the
performance threshold, an adjustment message for the storage
device. The adjustment message instructs the storage device to
adjust a performance parameter of the storage device. The compute
device is also to send the adjustment message to the storage
device.
Inventors: |
Sen; Sujoy; (Beaverton,
OR) ; Cain; Gamil; (El Dorado Hills, CA) ;
Greer; Teddy; (Freemont, CA) ; Chagam Reddy; Anjaneya
Reddy; (Chandler, AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
65037934 |
Appl. No.: |
15/858569 |
Filed: |
December 29, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62584401 |
Nov 10, 2017 |
|
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 9/28 20130101; G06F
12/0607 20130101; G06F 16/24553 20190101; H04L 9/0819 20130101;
G06F 12/0802 20130101; G06F 16/221 20190101; G06F 3/065 20130101;
G06F 15/17331 20130101; H04L 69/22 20130101; G06F 3/0644 20130101;
H04L 49/30 20130101; G06F 3/0647 20130101; G06F 9/4494 20180201;
H04L 41/0896 20130101; H04L 49/9005 20130101; G06F 2212/1044
20130101; H04L 45/7453 20130101; G06F 9/5088 20130101; G11C 29/44
20130101; G06F 9/445 20130101; G06F 3/0611 20130101; G06F 16/2255
20190101; G06F 30/34 20200101; G06F 3/0604 20130101; G06F 3/067
20130101; G06F 2212/1052 20130101; G06F 2201/85 20130101; H04L
69/12 20130101; H04L 69/321 20130101; G06F 16/119 20190101; G06F
16/2237 20190101; G06F 16/25 20190101; H04L 41/0213 20130101; H04L
41/0668 20130101; H04L 41/0893 20130101; H04L 49/351 20130101; G06F
3/0629 20130101; G06F 3/0631 20130101; G06F 15/161 20130101; G06F
2209/509 20130101; H04L 45/28 20130101; G06F 13/42 20130101; G06F
3/0605 20130101; G11C 29/028 20130101; Y02D 10/00 20180101; G06F
12/1063 20130101; H04L 47/125 20130101; H04L 69/32 20130101; G06F
16/2282 20190101; G06F 16/248 20190101; G06F 3/0613 20130101; G06F
9/5044 20130101; G06F 16/2453 20190101; G06F 16/2455 20190101; G06F
16/9014 20190101; G06F 3/0685 20130101; G06F 13/4068 20130101; G11C
29/36 20130101; G06F 12/06 20130101; G06F 13/1663 20130101; G06F
16/2365 20190101; G11C 8/12 20130101; H04L 41/0677 20130101; H04L
47/11 20130101; G06F 3/0659 20130101; G06F 2212/601 20130101; G06F
2213/0064 20130101; G11C 29/38 20130101; H04L 9/0894 20130101; G06F
9/4411 20130101; G06F 12/14 20130101; G06F 3/0632 20130101; G06F
3/0673 20130101; G06F 3/0683 20130101; G06F 9/4401 20130101; G06F
9/4406 20130101; G06F 12/023 20130101; G06F 12/1054 20130101; G06F
13/1668 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06; G11C 29/38 20060101 G11C029/38; G11C 29/44 20060101
G11C029/44 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 30, 2017 |
IN |
201741030632 |
Claims
1. A compute device to manage storage device performance, the
compute device comprising: a compute engine to: receive, from a
data storage sled, storage device data from a storage device
located on the data storage sled, wherein the storage device data
includes storage device self-test data that defines a result of a
self-test performed by the storage device; determine, in response
to the storage device self-test data, whether the storage device
fails to satisfy a performance threshold; generate, in response to
a determination that the storage device fails to satisfy the
performance threshold, an adjustment message for the storage
device, wherein the adjustment message instructs the storage device
to adjust a performance parameter of the storage device; and send
the adjustment message to the storage device.
2. The compute device of claim 1, wherein to generate the
adjustment message comprises to generate an adjustment message that
instructs the storage device to adjust a capacity provisioning
ratio for the storage device, wherein the capacity provisioning
ratio defines a ratio of usable storage space to provisionable
space for the storage device.
3. The compute device of claim 2, wherein the compute engine is to
set the capacity provisioning ratio for the storage device, and
wherein the compute engine is further to: receive performance
specification data from the storage device on a first startup of
the storage device, wherein the performance specification data
includes a performance target for the storage device; generate the
capacity provisioning ratio for the storage device as a function of
at least one of an anticipated device pool allocation for the
storage device and the performance target; and send the capacity
provisioning ratio to the storage device to cause the storage
device to apportion a usable storage space and a provisionable
space within a storage unit of the storage device as a function of
the capacity provisioning ratio.
4. The compute device of claim 1, wherein the storage device is
associated with a first device pool, and wherein the compute engine
is further to: determine, in response to receipt of the storage
device self-test data, whether the storage device fails to satisfy
a performance threshold for the first device pool; determine
whether the storage device satisfies a performance threshold for a
second device pool, different from the first device pool; and
reallocate, in response to to determination that the storage device
satisfies the performance threshold for the second device pool, the
storage device to the second device pool.
5. The compute device of claim 4, wherein a first drive writes per
day value for the first device pool exceeds a second drive writes
per day value for the second device pool.
6. The compute device of claim 1, wherein the compute engine is
further to: query a compute sled connected to the storage device
for a performance target of the storage device, wherein the
performance target is associated with a workload to be executed by
the compute sled; determine whether the storage device fails to
satisfy the performance target; and generate, in response to a
determination that the storage device fails to satisfy the
performance target, a second adjustment message for the storage
device, wherein the second adjustment message instructs the storage
device to satisfy the performance target associated with the
compute sled.
7. The compute device of claim 1, wherein to determine whether the
storage device fails to satisfy a performance threshold comprises
to: retrieve at least one of a latency value, an input-output per
second (IOPS) value, or a drive writes per day (DWPD) value from
the storage device self-test data; determine, as a function of
performance specification data that includes a performance target
for the storage device, at least one of a latency threshold, an
IOPS threshold, or a DWPD threshold; and compare one or more of the
latency value to the latency threshold, the IOPS value to the IOPS
threshold, or the DWPD value to the DWPD threshold.
8. The compute device of claim 1, wherein the compute engine is
further to: identify the one or more performance thresholds based
on performance specification data that includes a performance
target for the storage device; generate, as a function of the one
or more performance thresholds, a self-test for the storage device
to perform; and transmit the self-test to the storage device.
9. The compute device of claim 1, wherein to determine whether the
storage device fails to satisfy a performance threshold comprises
to: retrieve at least one of a latency value, an input-output per
second (IOPS) value, or a drive writes per day (DWPD) value from
the storage device self-test data; determine, as a function of
performance specification data that includes a performance target
for the storage device, at least one of a latency threshold, an
IOPS threshold, or a DWPD threshold; and compare one or more of the
latency value to the latency threshold, the IOPS value to the IOPS
threshold, or the DWPD value to the DWPD threshold.
10. The compute device of claim 1, wherein the compute engine is
further to: identify the one or more performance thresholds based
on performance specification data that includes a performance
target for the storage device; generate, as a function of the one
or more performance thresholds, a self-test for the storage device
to perform; and transmit the self-test to the storage device.
11. One or more machine-readable storage media comprising a
plurality of instructions stored thereon that, in response to being
executed, cause a compute device to: receive, from a data storage
sled, storage device data from a storage device located on the data
storage sled, wherein the storage device data includes storage
device self-test data that defines a result of a self-test
performed by the storage device; determine, in response to the
storage device self-test data, whether the storage device fails to
satisfy a performance threshold; generate, in response to a
determination that the storage device fails to satisfy the
performance threshold, an adjustment message for the storage
device, wherein the adjustment message instructs the storage device
to adjust a performance parameter of the storage device; and send
the adjustment message to the storage device.
12. The one or more machine-readable storage media of claim 11,
wherein the plurality of instructions further cause compute device
to generate an adjustment message that instructs the storage device
to adjust a capacity provisioning ratio for the storage device,
wherein the capacity provisioning ratio defines a ratio of usable
storage space to provisionable space for the storage device.
13. The one or more machine-readable storage media of claim 11,
wherein the plurality of instructions further cause the compute
device to: set the capacity provisioning ratio for the storage
device; receive performance specification data from the storage
device on a first startup of the storage device, wherein the
performance specification data includes a performance target for
the storage device; generate the capacity provisioning ratio for
the storage device as a function of at least one of an anticipated
device pool allocation for the storage device and the performance
target; and send the capacity provisioning ratio to the storage
device to cause the storage device to apportion a usable storage
space and a provisionable space within a storage unit of the
storage device as a function of the capacity provisioning
ratio.
14. The one or more machine-readable storage media of claim 11,
wherein the storage device is associated with a first device pool,
and wherein the plurality of instructions further cause the compute
device to: determine, in response to receipt of the storage device
self-test data, whether the storage device fails to satisfy a
performance threshold for the first device pool; determine whether
the storage device satisfies a performance threshold for a second
device pool, different from the first device pool; and reallocate,
in response to to determination that the storage device satisfies
the performance threshold for the second device pool, the storage
device to the second device pool.
15. The one or more machine-readable storage media of claim 11,
wherein a first drive writes per day value for the first device
pool exceeds a second drive writes per day value for the second
device pool.
16. The one or more machine-readable storage media of claim 11,
wherein the plurality of instructions further cause the compute
device to: query a compute sled connected to the storage device for
a performance target of the storage device, wherein the performance
target is associated with a workload to be executed by the compute
sled; determine whether the storage device fails to satisfy the
performance target; and generate, in response to a determination
that the storage device fails to satisfy the performance target, a
second adjustment message for the storage device, wherein the
second adjustment message instructs the storage device to satisfy
the performance target associated with the compute sled.
17. The one or more machine-readable storage media of claim 11,
wherein the plurality of instructions further cause the compute
device to: retrieve at least one of a latency value, an
input-output per second (IOPS) value, or a drive writes per day
(DWPD) value from the storage device self-test data; determine, as
a function of performance specification data that includes a
performance target for the storage device, at least one of a
latency threshold, an IOPS threshold, or a DWPD threshold; and
compare one or more of the latency value to the latency threshold,
the IOPS value to the IOPS threshold, or the DWPD value to the DWPD
threshold.
18. The one or more machine-readable storage media of claim 11,
wherein the plurality of instructions further cause the compute
device to: identify the one or more performance thresholds based on
performance specification data that includes a performance target
for the storage device; generate, as a function of the one or more
performance thresholds, a self-test for the storage device to
perform; and transmit the self-test to the storage device.
19. A method to manage storage device performance, the method
comprising: receive, from a data storage sled, storage device data
from a storage device located on the data storage sled, wherein the
storage device data includes storage device self-test data that
defines a result of a self-test performed by the storage device;
determine, in response to the storage device self-test data,
whether the storage device fails to satisfy a performance
threshold; generate, in response to a determination that the
storage device fails to satisfy the performance threshold, an
adjustment message for the storage device, wherein the adjustment
message instructs the storage device to adjust a performance
parameter of the storage device; and send the adjustment message to
the storage device.
20. The method of claim 19, wherein generating the adjustment
message comprises generating an adjustment message that instructs
the storage device to adjust a capacity provisioning ratio for the
storage device, wherein the capacity provisioning ratio defines a
ratio of usable storage space to provisionable space for the
storage device.
21. The method of claim 19, further comprising: receiving, by the
compute device, performance specification data from the storage
device on a first startup of the storage device, wherein the
performance specification data includes a performance target for
the storage device; generating, by the compute device, the capacity
provisioning ratio for the storage device as a function of at least
one of an anticipated device pool allocation for the storage device
and the performance target; and sending, by the compute device, the
capacity provisioning ratio to the storage device to cause the
storage device to apportion a usable storage space and a
provisionable space within a storage unit of the storage device as
a function of the capacity provisioning ratio.
22. The method of claim 19, wherein the storage device is
associated with a first device pool, the method further comprising:
determining, by the compute device and in response to receipt of
the storage device self-test data, whether the storage device fails
to satisfy a performance threshold for the first device pool;
determining, by the compute device, whether the storage device
satisfies a performance threshold for a second device pool,
different from the first device pool; and reallocating, by the
compute device and in response to a determination that the storage
device satisfies the performance threshold for the second device
pool, the storage device to the second device pool.
23. The method of claim 19, wherein generating the adjustment
message comprises generating an adjustment message to reallocate
the storage device from a write-intensive storage device pool to a
read-intensive storage device pool.
24. The method of claim 23, wherein a first drive writes per day
value for the first device pool exceeds a second drive writes per
day value for the second device pool.
25. The method of claim 19, further comprising: querying, by the
compute device, a compute sled connected to the storage device for
a performance target of the storage device, wherein the performance
target is associated with a workload to be executed by the compute
sled; determining, by the compute device, whether the storage
device fails to satisfy the performance target; and generating, by
the compute device and in response to a determination that the
storage device fails to satisfy the performance target, a second
adjustment message for the storage device, wherein the second
adjustment message instructs the storage device to satisfy the
performance target associated with the compute sled.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims the benefit of Indian
Provisional Patent Application No. 201741030632, filed Aug. 30,
2017 and U.S. Provisional Patent Application No. 62/584,401, filed
Nov. 10, 2017.
BACKGROUND
[0002] In large computer networks, storage devices perform
important functions such as storing data for use by applications
that may operate on other compute devices. Data for one application
may be distributed across multiple storage devices. High
performance, reliability, and security are essential attributes for
these storage devices. Performance capabilities such as size,
latency, and throughput are generally specified by the manufacturer
of a storage device or unit (e.g., a hard disk drive or solid-state
drive). For example, the lower the usable storage capacity for a
given amount of media, the better its write performance can be.
[0003] However, these capabilities are often recorded on a document
(e.g., a performance specification sheet) that is provided with the
storage device and may be difficult to access or query. Making
useful decisions regarding a large number of storage devices may
present obstacles because retrieving performance specification data
for each storage device can be a cumbersome and labor-intensive
process. Additionally, it is time-consuming to assess a storage
device's performance on an ongoing basis. Some known methods
involve querying or externally testing a storage device to
determine whether it is meeting performance requirements. This,
too, becomes untenable at the scale at which many modern computer
networks employ storage devices, because performing testing and
later collating results can be a tedious and expensive process.
Additionally, even after testing, it may be unfeasible to
reconfigure a storage device for continued performance, resulting
in the storage device being decommissioned while it still has
usable life.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The concepts described herein are illustrated by way of
example and not by way of limitation in the accompanying figures.
For simplicity and clarity of illustration, elements illustrated in
the figures are not necessarily drawn to scale. Where considered
appropriate, reference labels have been repeated among the figures
to indicate corresponding or analogous elements.
[0005] FIG. 1 is a diagram of a conceptual overview of a data
center in which one or more techniques described herein may be
implemented according to various embodiments;
[0006] FIG. 2 is a diagram of an example embodiment of a logical
configuration of a rack of the data center of FIG. 1;
[0007] FIG. 3 is a diagram of an example embodiment of another data
center in which one or more techniques described herein may be
implemented according to various embodiments;
[0008] FIG. 4 is a diagram of another example embodiment of a data
center in which one or more techniques described herein may be
implemented according to various embodiments;
[0009] FIG. 5 is a diagram of a connectivity scheme representative
of link-layer connectivity that may be established among various
sleds of the data centers of FIGS. 1, 3, and 4;
[0010] FIG. 6 is a diagram of a rack architecture that may be
representative of an architecture of any particular one of the
racks depicted in FIGS. 1-4 according to some embodiments;
[0011] FIG. 7 is a diagram of an example embodiment of a sled that
may be used with the rack architecture of FIG. 6;
[0012] FIG. 8 is a diagram of an example embodiment of a rack
architecture to provide support for sleds featuring expansion
capabilities;
[0013] FIG. 9 is a diagram of an example embodiment of a rack
implemented according to the rack architecture of FIG. 8;
[0014] FIG. 10 is a diagram of an example embodiment of a sled
designed for use in conjunction with the rack of FIG. 9;
[0015] FIG. 11 is a diagram of an example embodiment of a data
center in which one or more techniques described herein may be
implemented according to various embodiments;
[0016] FIG. 12 is a simplified block diagram of at least one
embodiment of a system for storage discovery and automatic
repurposing of storage devices;
[0017] FIG. 13 is a simplified block diagram of at least one
embodiment of a storage device of the system of FIG. 12;
[0018] FIG. 14 is a simplified block diagram of at least one
embodiment of an environment that may be established by the storage
device of FIGS. 12 and 13;
[0019] FIG. 15 is a simplified block diagram of at least one
embodiment of an environment that may be established by the
orchestrator server of FIG. 12;
[0020] FIGS. 16 and 17 are simplified flow diagrams of at least one
embodiment of a method for providing storage discovery and
automatic repurposing of storage devices that may be performed by
the storage device of FIGS. 12-14; and
[0021] FIGS. 18 and 19 are simplified flow diagrams of at least one
embodiment of a method for providing storage discovery and
automatic repurposing of storage devices that may be performed by
the orchestrator server of FIGS. 12-14.
DETAILED DESCRIPTION OF THE DRAWINGS
[0022] While the concepts of the present disclosure are susceptible
to various modifications and alternative forms, specific
embodiments thereof have been shown by way of example in the
drawings and will be described herein in detail. It should be
understood, however, that there is no intent to limit the concepts
of the present disclosure to the particular forms disclosed, but on
the contrary, the intention is to cover all modifications,
equivalents, and alternatives consistent with the present
disclosure and the appended claims.
[0023] References in the specification to "one embodiment," "an
embodiment," "an illustrative embodiment," etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may or may not necessarily
include that particular feature, structure, or characteristic.
Moreover, such phrases are not necessarily referring to the same
embodiment. Further, when a particular feature, structure, or
characteristic is described in connection with an embodiment, it is
submitted that it is within the knowledge of one skilled in the art
to effect such feature, structure, or characteristic in connection
with other embodiments whether or not explicitly described.
Additionally, it should be appreciated that items included in a
list in the form of "at least one A, B, and C" can mean (A); (B);
(C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly,
items listed in the form of "at least one of A, B, or C" can mean
(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and
C).
[0024] The disclosed embodiments may be implemented, in some cases,
in hardware, firmware, software, or any combination thereof. The
disclosed embodiments may also be implemented as instructions
carried by or stored on a transitory or non-transitory
machine-readable (e.g., computer-readable) storage medium, which
may be read and executed by one or more processors. A
machine-readable storage medium may be embodied as any storage
device, mechanism, or other physical structure for storing or
transmitting information in a form readable by a machine (e.g., a
volatile or non-volatile memory, a media disc, or other media
device).
[0025] In the drawings, some structural or method features may be
shown in specific arrangements and/or orderings. However, it should
be appreciated that such specific arrangements and/or orderings may
not be required. Rather, in some embodiments, such features may be
arranged in a different manner and/or order than shown in the
illustrative figures. Additionally, the inclusion of a structural
or method feature in a particular figure is not meant to imply that
such feature is required in all embodiments and, in some
embodiments, may not be included or may be combined with other
features.
[0026] FIG. 1 illustrates a conceptual overview of a data center
100 that may generally be representative of a data center or other
type of computing network in/for which one or more techniques
described herein may be implemented according to various
embodiments. As shown in FIG. 1, data center 100 may generally
contain a plurality of racks, each of which may house computing
equipment comprising a respective set of physical resources. In the
particular non-limiting example depicted in FIG. 1, data center 100
contains four racks 102A to 102D, which house computing equipment
comprising respective sets of physical resources (PCRs) 105A to
105D. According to this example, a collective set of physical
resources 106 of data center 100 includes the various sets of
physical resources 105A to 105D that are distributed among racks
102A to 102D. Physical resources 106 may include resources of
multiple types, such as--for example--processors, co-processors,
accelerators, field programmable gate arrays (FPGAs), memory, and
storage. The embodiments are not limited to these examples.
[0027] The illustrative data center 100 differs from typical data
centers in many ways. For example, in the illustrative embodiment,
the circuit boards ("sleds") on which components such as CPUs,
memory, and other components are placed for increased thermal
performance In particular, in the illustrative embodiment, the
sleds are shallower than typical boards. In other words, the sleds
are shorter from the front to the back, where cooling fans are
located. This decreases the length of the path that air must to
travel across the components on the board. Further, the components
on the sled are spaced further apart than in typical circuit
boards, and the components are arranged to reduce or eliminate
shadowing (i.e., one component in the air flow path of another
component). In the illustrative embodiment, processing components
such as the processors are located on a top side of a sled while
near memory, such as DIMMs, are located on a bottom side of the
sled. As a result of the enhanced airflow provided by this design,
the components may operate at higher frequencies and power levels
than in typical systems, thereby increasing performance
Furthermore, the sleds are configured to blindly mate with power
and data communication cables in each rack 102A, 102B, 102C, 102D,
enhancing their ability to be quickly removed, upgraded,
reinstalled, and/or replaced. Similarly, individual components
located on the sleds, such as processors, accelerators, memory, and
data storage drives, are configured to be easily upgraded due to
their increased spacing from each other. In the illustrative
embodiment, the components additionally include hardware
attestation features to prove their authenticity.
[0028] Furthermore, in the illustrative embodiment, the data center
100 utilizes a single network architecture ("fabric") that supports
multiple other network architectures including Ethernet and
Omni-Path. The sleds, in the illustrative embodiment, are coupled
to switches via optical fibers, which provide higher bandwidth and
lower latency than typical twisted pair cabling (e.g., Category 5,
Category 5e, Category 6, etc.). Due to the high bandwidth, low
latency interconnections and network architecture, the data center
100 may, in use, pool resources, such as memory, accelerators
(e.g., graphics accelerators, FPGAs, ASICs, etc.), and data storage
drives that are physically disaggregated, and provide them to
compute resources (e.g., processors) on an as needed basis,
enabling the compute resources to access the pooled resources as if
they were local. The illustrative data center 100 additionally
receives utilization information for the various resources,
predicts resource utilization for different types of workloads
based on past resource utilization, and dynamically reallocates the
resources based on this information.
[0029] The racks 102A, 102B, 102C, 102D of the data center 100 may
include physical design features that facilitate the automation of
a variety of types of maintenance tasks. For example, data center
100 may be implemented using racks that are designed to be
robotically-accessed, and to accept and house
robotically-manipulatable resource sleds. Furthermore, in the
illustrative embodiment, the racks 102A, 102B, 102C, 102D include
integrated power sources that receive a greater voltage than is
typical for power sources. The increased voltage enables the power
sources to provide additional power to the components on each sled,
enabling the components to operate at higher than typical
frequencies.
[0030] FIG. 2 illustrates an exemplary logical configuration of a
rack 202 of the data center 100. As shown in FIG. 2, rack 202 may
generally house a plurality of sleds, each of which may comprise a
respective set of physical resources. In the particular
non-limiting example depicted in FIG. 2, rack 202 houses sleds
204-1 to 204-4 comprising respective sets of physical resources
205-1 to 205-4, each of which constitutes a portion of the
collective set of physical resources 206 comprised in rack 202.
With respect to FIG. 1, if rack 202 is representative of--for
example--rack 102A, then physical resources 206 may correspond to
the physical resources 105A comprised in rack 102A. In the context
of this example, physical resources 105A may thus be made up of the
respective sets of physical resources, including physical storage
resources 205-1, physical accelerator resources 205-2, physical
memory resources 205-3, and physical compute resources 205-4
comprised in the sleds 204-1 to 204-4 of rack 202. The embodiments
are not limited to this example. Each sled may contain a pool of
each of the various types of physical resources (e.g., compute,
memory, accelerator, storage). By having robotically accessible and
robotically manipulatable sleds comprising disaggregated resources,
each type of resource can be upgraded independently of each other
and at their own optimized refresh rate.
[0031] FIG. 3 illustrates an example of a data center 300 that may
generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. In the particular non-limiting example depicted in
FIG. 3, data center 300 comprises racks 302-1 to 302-32. In various
embodiments, the racks of data center 300 may be arranged in such
fashion as to define and/or accommodate various access pathways.
For example, as shown in FIG. 3, the racks of data center 300 may
be arranged in such fashion as to define and/or accommodate access
pathways 311A, 311B, 311C, and 311D. In some embodiments, the
presence of such access pathways may generally enable automated
maintenance equipment, such as robotic maintenance equipment, to
physically access the computing equipment housed in the various
racks of data center 300 and perform automated maintenance tasks
(e.g., replace a failed sled, upgrade a sled). In various
embodiments, the dimensions of access pathways 311A, 311B, 311C,
and 311D, the dimensions of racks 302-1 to 302-32, and/or one or
more other aspects of the physical layout of data center 300 may be
selected to facilitate such automated operations. The embodiments
are not limited in this context.
[0032] FIG. 4 illustrates an example of a data center 400 that may
generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. As shown in FIG. 4, data center 400 may feature an
optical fabric 412. Optical fabric 412 may generally comprise a
combination of optical signaling media (such as optical cabling)
and optical switching infrastructure via which any particular sled
in data center 400 can send signals to (and receive signals from)
each of the other sleds in data center 400. The signaling
connectivity that optical fabric 412 provides to any given sled may
include connectivity both to other sleds in a same rack and sleds
in other racks. In the particular non-limiting example depicted in
FIG. 4, data center 400 includes four racks 402A to 402D. Racks
402A to 402D house respective pairs of sleds 404A-1 and 404A-2,
404B-1 and 404B-2, 404C-1 and 404C-2, and 404D-1 and 404D-2. Thus,
in this example, data center 400 comprises a total of eight sleds.
Via optical fabric 412, each such sled may possess signaling
connectivity with each of the seven other sleds in data center 400.
For example, via optical fabric 412, sled 404A-1 in rack 402A may
possess signaling connectivity with sled 404A-2 in rack 402A, as
well as the six other sleds 404B-1, 404B-2, 404C-1, 404C-2, 404D-1,
and 404D-2 that are distributed among the other racks 402B, 402C,
and 402D of data center 400. The embodiments are not limited to
this example.
[0033] FIG. 5 illustrates an overview of a connectivity scheme 500
that may generally be representative of link-layer connectivity
that may be established in some embodiments among the various sleds
of a data center, such as any of example data centers 100, 300, and
400 of FIGS. 1, 3, and 4. Connectivity scheme 500 may be
implemented using an optical fabric that features a dual-mode
optical switching infrastructure 514. Dual-mode optical switching
infrastructure 514 may generally comprise a switching
infrastructure that is capable of receiving communications
according to multiple link-layer protocols via a same unified set
of optical signaling media, and properly switching such
communications. In various embodiments, dual-mode optical switching
infrastructure 514 may be implemented using one or more dual-mode
optical switches 515. In various embodiments, dual-mode optical
switches 515 may generally comprise high-radix switches. In some
embodiments, dual-mode optical switches 515 may comprise multi-ply
switches, such as four-ply switches. In various embodiments,
dual-mode optical switches 515 may feature integrated silicon
photonics that enable them to switch communications with
significantly reduced latency in comparison to conventional
switching devices. In some embodiments, dual-mode optical switches
515 may constitute leaf switches 530 in a leaf-spine architecture
additionally including one or more dual-mode optical spine switches
520.
[0034] In various embodiments, dual-mode optical switches may be
capable of receiving both Ethernet protocol communications carrying
Internet Protocol (IP packets) and communications according to a
second, high-performance computing (HPC) link-layer protocol (e.g.,
Intel's Omni-Path Architecture's, InfiniBand.TM.) via optical
signaling media of an optical fabric. As reflected in FIG. 5, with
respect to any particular pair of sleds 504A and 504B possessing
optical signaling connectivity to the optical fabric, connectivity
scheme 500 may thus provide support for link-layer connectivity via
both Ethernet links and HPC links. Thus, both Ethernet and HPC
communications can be supported by a single high-bandwidth,
low-latency switch fabric. The embodiments are not limited to this
example.
[0035] FIG. 6 illustrates a general overview of a rack architecture
600 that may be representative of an architecture of any particular
one of the racks depicted in FIGS. 1 to 4 according to some
embodiments. As reflected in FIG. 6, rack architecture 600 may
generally feature a plurality of sled spaces into which sleds may
be inserted, each of which may be robotically-accessible via a rack
access region 601. In the particular non-limiting example depicted
in FIG. 6, rack architecture 600 features five sled spaces 603-1 to
603-5. Sled spaces 603-1 to 603-5 feature respective multi-purpose
connector modules (MPCMs) 616-1 to 616-5.
[0036] FIG. 7 illustrates an example of a sled 704 that may be
representative of a sled of such a type. As shown in FIG. 7, sled
704 may comprise a set of physical resources 705, as well as an
MPCM 716 designed to couple with a counterpart MPCM when sled 704
is inserted into a sled space such as any of sled spaces 603-1 to
603-5 of FIG. 6. Sled 704 may also feature an expansion connector
717. Expansion connector 717 may generally comprise a socket, slot,
or other type of connection element that is capable of accepting
one or more types of expansion modules, such as an expansion sled
718. By coupling with a counterpart connector on expansion sled
718, expansion connector 717 may provide physical resources 705
with access to supplemental computing resources 705B residing on
expansion sled 718. The embodiments are not limited in this
context.
[0037] FIG. 8 illustrates an example of a rack architecture 800
that may be representative of a rack architecture that may be
implemented in order to provide support for sleds featuring
expansion capabilities, such as sled 704 of FIG. 7. In the
particular non-limiting example depicted in FIG. 8, rack
architecture 800 includes seven sled spaces 803-1 to 803-7, which
feature respective MPCMs 816-1 to 816-7. Sled spaces 803-1 to 803-7
include respective primary regions 803-1A to 803-7A and respective
expansion regions 803-1B to 803-7B. With respect to each such sled
space, when the corresponding MPCM is coupled with a counterpart
MPCM of an inserted sled, the primary region may generally
constitute a region of the sled space that physically accommodates
the inserted sled. The expansion region may generally constitute a
region of the sled space that can physically accommodate an
expansion module, such as expansion sled 718 of FIG. 7, in the
event that the inserted sled is configured with such a module.
[0038] FIG. 9 illustrates an example of a rack 902 that may be
representative of a rack implemented according to rack architecture
800 of FIG. 8 according to some embodiments. In the particular
non-limiting example depicted in FIG. 9, rack 902 features seven
sled spaces 903-1 to 903-7, which include respective primary
regions 903-1A to 903-7A and respective expansion regions 903-1B to
903-7B. In various embodiments, temperature control in rack 902 may
be implemented using an air cooling system. For example, as
reflected in FIG. 9, rack 902 may feature a plurality of fans 921
that are generally arranged to provide air cooling within the
various sled spaces 903-1 to 903-7. In some embodiments, the height
of the sled space is greater than the conventional "1U" server
height. In such embodiments, fans 921 may generally comprise
relatively slow, large diameter cooling fans as compared to fans
used in conventional rack configurations. Running larger diameter
cooling fans at lower speeds may increase fan lifetime relative to
smaller diameter cooling fans running at higher speeds while still
providing the same amount of cooling. The sleds are physically
shallower than conventional rack dimensions. Further, components
are arranged on each sled to reduce thermal shadowing (i.e., not
arranged serially in the direction of air flow). As a result, the
wider, shallower sleds allow for an increase in device performance
because the devices can be operated at a higher thermal envelope
(e.g., 250W) due to improved cooling (i.e., no thermal shadowing,
more space between devices, more room for larger heat sinks,
etc.).
[0039] MPCMs 916-1 to 916-7 may be configured to provide inserted
sleds with access to power sourced by respective power modules
920-1 to 920-7, each of which may draw power from an external power
source 919. In various embodiments, external power source 919 may
deliver alternating current (AC) power to rack 902, and power
modules 920-1 to 920-7 may be configured to convert such AC power
to direct current (DC) power to be sourced to inserted sleds. In
some embodiments, for example, power modules 920-1 to 920-7 may be
configured to convert 277-volt AC power into 12-volt DC power for
provision to inserted sleds via respective MPCMs 916-1 to 916-7.
The embodiments are not limited to this example.
[0040] MPCMs 916-1 to 916-7 may also be arranged to provide
inserted sleds with optical signaling connectivity to a dual-mode
optical switching infrastructure 914, which may be the same as--or
similar to--dual-mode optical switching infrastructure 514 of FIG.
5. In various embodiments, optical connectors contained in MPCMs
916-1 to 916-7 may be designed to couple with counterpart optical
connectors contained in MPCMs of inserted sleds to provide such
sleds with optical signaling connectivity to dual-mode optical
switching infrastructure 914 via respective lengths of optical
cabling 922-1 to 922-7. In some embodiments, each such length of
optical cabling may extend from its corresponding MPCM to an
optical interconnect loom 923 that is external to the sled spaces
of rack 902. In various embodiments, optical interconnect loom 923
may be arranged to pass through a support post or other type of
load-bearing element of rack 902. The embodiments are not limited
in this context. Because inserted sleds connect to an optical
switching infrastructure via MPCMs, the resources typically spent
in manually configuring the rack cabling to accommodate a newly
inserted sled can be saved.
[0041] FIG. 10 illustrates an example of a sled 1004 that may be
representative of a sled designed for use in conjunction with rack
902 of FIG. 9 according to some embodiments. Sled 1004 may feature
an MPCM 1016 that comprises an optical connector 1016A and a power
connector 1016B, and that is designed to couple with a counterpart
MPCM of a sled space in conjunction with insertion of MPCM 1016
into that sled space. Coupling MPCM 1016 with such a counterpart
MPCM may cause power connector 1016 to couple with a power
connector comprised in the counterpart MPCM. This may generally
enable physical resources 1005 of sled 1004 to source power from an
external source, via power connector 1016 and power transmission
media 1024 that conductively couples power connector 1016 to
physical resources 1005.
[0042] Sled 1004 may also include dual-mode optical network
interface circuitry 1026. Dual-mode optical network interface
circuitry 1026 may generally comprise circuitry that is capable of
communicating over optical signaling media according to each of
multiple link-layer protocols supported by dual-mode optical
switching infrastructure 914 of FIG. 9. In some embodiments,
dual-mode optical network interface circuitry 1026 may be capable
both of Ethernet protocol communications and of communications
according to a second, high-performance protocol. In various
embodiments, dual-mode optical network interface circuitry 1026 may
include one or more optical transceiver modules 1027, each of which
may be capable of transmitting and receiving optical signals over
each of one or more optical channels. The embodiments are not
limited in this context.
[0043] Coupling MPCM 1016 with a counterpart MPCM of a sled space
in a given rack may cause optical connector 1016A to couple with an
optical connector comprised in the counterpart MPCM. This may
generally establish optical connectivity between optical cabling of
the sled and dual-mode optical network interface circuitry 1026,
via each of a set of optical channels 1025. Dual-mode optical
network interface circuitry 1026 may communicate with the physical
resources 1005 of sled 1004 via electrical signaling media 1028. In
addition to the dimensions of the sleds and arrangement of
components on the sleds to provide improved cooling and enable
operation at a relatively higher thermal envelope (e.g., 250W), as
described above with reference to FIG. 9, in some embodiments, a
sled may include one or more additional features to facilitate air
cooling, such as a heatpipe and/or heat sinks arranged to dissipate
heat generated by physical resources 1005. It is worthy of note
that although the example sled 1004 depicted in FIG. 10 does not
feature an expansion connector, any given sled that features the
design elements of sled 1004 may also feature an expansion
connector according to some embodiments. The embodiments are not
limited in this context.
[0044] FIG. 11 illustrates an example of a data center 1100 that
may generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. As reflected in FIG. 11, a physical infrastructure
management framework 1150A may be implemented to facilitate
management of a physical infrastructure 1100A of data center 1100.
In various embodiments, one function of physical infrastructure
management framework 1150A may be to manage automated maintenance
functions within data center 1100, such as the use of robotic
maintenance equipment to service computing equipment within
physical infrastructure 1100A. In some embodiments, physical
infrastructure 1100A may feature an advanced telemetry system that
performs telemetry reporting that is sufficiently robust to support
remote automated management of physical infrastructure 1100A. In
various embodiments, telemetry information provided by such an
advanced telemetry system may support features such as failure
prediction/prevention capabilities and capacity planning
capabilities. In some embodiments, physical infrastructure
management framework 1150A may also be configured to manage
authentication of physical infrastructure components using hardware
attestation techniques. For example, robots may verify the
authenticity of components before installation by analyzing
information collected from a radio frequency identification (RFID)
tag associated with each component to be installed. The embodiments
are not limited in this context.
[0045] As shown in FIG. 11, the physical infrastructure 1100A of
data center 1100 may comprise an optical fabric 1112, which may
include a dual-mode optical switching infrastructure 1114. Optical
fabric 1112 and dual-mode optical switching infrastructure 1114 may
be the same as--or similar to--optical fabric 412 of FIG. 4 and
dual-mode optical switching infrastructure 514 of FIG. 5,
respectively, and may provide high-bandwidth, low-latency,
multi-protocol connectivity among sleds of data center 1100. As
discussed above, with reference to FIG. 1, in various embodiments,
the availability of such connectivity may make it feasible to
disaggregate and dynamically pool resources such as accelerators,
memory, and storage. In some embodiments, for example, one or more
pooled accelerator sleds 1130 may be included among the physical
infrastructure 1100A of data center 1100, each of which may
comprise a pool of accelerator resources--such as co-processors
and/or FPGAs, for example--that is globally accessible to other
sleds via optical fabric 1112 and dual-mode optical switching
infrastructure 1114.
[0046] In another example, in various embodiments, one or more
pooled storage sleds 1132 may be included among the physical
infrastructure 1100A of data center 1100, each of which may
comprise a pool of storage resources that is globally accessible to
other sleds via optical fabric 1112 and dual-mode optical switching
infrastructure 1114. In some embodiments, such pooled storage sleds
1132 may comprise pools of solid-state storage devices such as
solid-state drives (SSDs). In various embodiments, one or more
high-performance processing sleds 1134 may be included among the
physical infrastructure 1100A of data center 1100. In some
embodiments, high-performance processing sleds 1134 may comprise
pools of high-performance processors, as well as cooling features
that enhance air cooling to yield a higher thermal envelope of up
to 250W or more. In various embodiments, any given high-performance
processing sled 1134 may feature an expansion connector 1117 that
can accept a far memory expansion sled, such that the far memory
that is locally available to that high-performance processing sled
1134 is disaggregated from the processors and near memory comprised
on that sled. In some embodiments, such a high-performance
processing sled 1134 may be configured with far memory using an
expansion sled that comprises low-latency SSD storage. The optical
infrastructure allows for compute resources on one sled to utilize
remote accelerator/FPGA, memory, and/or SSD resources that are
disaggregated on a sled located on the same rack or any other rack
in the data center. The remote resources can be located one switch
jump away or two-switch jumps away in the spine-leaf network
architecture described above with reference to FIG. 5. The
embodiments are not limited in this context.
[0047] In various embodiments, one or more layers of abstraction
may be applied to the physical resources of physical infrastructure
1100A in order to define a virtual infrastructure, such as a
software-defined infrastructure 1100B. In some embodiments, virtual
computing resources 1136 of software-defined infrastructure 1100B
may be allocated to support the provision of cloud services 1140.
In various embodiments, particular sets of virtual computing
resources 1136 may be grouped for provision to cloud services 1140
in the form of software-defined infrastructure (SDI) services 1138.
Examples of cloud services 1140 may include--without
limitation--software as a service (SaaS) services 1142, platform as
a service (PaaS) services 1144, and infrastructure as a service
(IaaS) services 1146.
[0048] In some embodiments, management of software-defined
infrastructure 1100B may be conducted using a virtual
infrastructure management framework 1150B. In various embodiments,
virtual infrastructure management framework 1150B may be designed
to implement workload fingerprinting techniques and/or
machine-learning techniques in conjunction with managing allocation
of virtual computing resources 1136 and/or SDI services 1138 to
cloud services 1140. In some embodiments, virtual infrastructure
management framework 1150B may use/consult telemetry data in
conjunction with performing such resource allocation. In various
embodiments, an application/service management framework 1150C may
be implemented in order to provide QoS management capabilities for
cloud services 1140. The embodiments are not limited in this
context.
[0049] Referring now to FIG. 12, a system 1200 for storage
discovery and automatic repurposing of storage devices may be
implemented in accordance with the data centers 100, 300, 400, 1100
described above with reference to FIGS. 1, 3, 4, and 11. In the
illustrative embodiment, the system 1200 includes an orchestrator
server 1202 communicatively coupled to multiple storage sleds
including a storage sled 1210 and a storage sled 1220. While two
storage sleds 1210, 1220 are shown, it should be understood that in
other embodiments, the system 1200 may include a different number
of storage sleds. One or more of the sleds 1210, 1220 may be
grouped into a managed node, such as by the orchestrator server
1202, to collectively perform a workload (e.g., the application
1208). A managed node may be embodied as an assembly of resources
(e.g., physical resources 206), such as compute resources (e.g.,
physical compute resources 205-4), memory resources (e.g., physical
memory resources 205-3), storage resources (e.g., physical storage
resources 205-1), or other resources (e.g., physical accelerator
resources 205-2), from the same or different sleds (e.g., the sleds
204-1, 204-2, 204-3, 204-4, etc.) or racks (e.g., one or more of
racks 302-1 through 302-32). Further, a managed node may be
established, defined, or "spun up" by the orchestrator server 1202
at the time a workload is to be assigned to the managed node or at
any other time, and may exist regardless of whether any workloads
are presently assigned to the managed node. The system 1200 may be
located in a data center and provide storage and compute services
(e.g., cloud services) to a compute sled 1204 that is in
communication with the system 1200 through a network 1290. The
orchestrator server 1202 may support a cloud operating environment,
such as OpenStack, and managed nodes established by the
orchestrator server 1202 may execute one or more applications or
processes (i.e., workloads), such as in virtual machines or
containers, on behalf of a user of a client device (not shown).
[0050] In the illustrative embodiment, the storage sled 1210 is
similar to the sled 204-1 of FIG. 2, and, in operation, executes an
application 1208 (e.g., a workload). The storage sleds 1210, 1220,
in the illustrative embodiment, each include one or more storage
devices 1230, which in turn include a storage drive controller 1214
and one or more storage units 1218. Each storage drive controller
1214, in the illustrative embodiment, includes a storage health
unit 1216. In the illustrative embodiment, the storage health unit
1216 which may be embodied as any device or circuitry (e.g., a
processor, an FPGA, an ASIC, etc.) capable of executing the
functions described in more detail below, with respect to FIG. 13.
In addition, the storage device 1230 may be divided into smaller
instances of storage devices, with each instance having its own set
of resources (e.g., its own subsidiary storage units 1218).
[0051] As stated above, each storage device 1230 also includes a
storage unit 1218. In the illustrative embodiment, a storage unit
1218 includes non-volatile memory (e.g., non-volatile
write-in-place byte-addressable memory, solid-state drives (SSDs),
and/or hard disk devices such as hard disk drives (HDDs). More
specifically, a storage unit 1218, in the illustrative embodiment,
may be embodied as a set of storage cells that are partitioned into
provisionable space 1222 and usable storage capacity 1224. The
usable storage capacity 1224 may be embodied as storage space to
that is accessible to an application (e.g., to the compute sled
1204 executing the application 1208 to read and/or write persistent
data. Over time, storage cells that are continuously used tend to
wear out and become less reliable cells for storage. The
provisionable space 1222 may be embodied as a sector, partition, or
sub-part of the storage unit 1218 that is not presently allocated
for access by an application (e.g., by the compute sled 1204
executing the application 1208), but includes spare storage cells
that can replace worn-out storage cells from the usable storage
capacity 1224.
[0052] Referring now to FIG. 13, the storage device 1230 may be
embodied as any type of device or circuitry capable of performing
the functions described herein, including obtaining performance
specification data from performance specification documentation
stored within the storage device 1230, performing a self-test,
where the self-test is provided by, for example, an orchestrator
server (e.g., the orchestrator server 1202) associated with the
storage device 1230, reporting a self-test result of the self-test
to a compute device (e.g., the orchestrator server 1202), receiving
an adjustment message from the compute device (e.g., the
orchestrator server 1202), and updating a storage device
performance parameter in response to the adjustment message. As
shown in FIG. 13, the illustrative storage device 1230 includes a
compute engine 1302, an input/output (I/O) subsystem 1308,
communication circuitry 1310, and one or more storage units 1218.
Additionally, in some embodiments, one or more of the illustrative
components may be incorporated in, or otherwise form a portion of,
another component.
[0053] The compute engine 1302 may be embodied as any type of
device or collection of devices capable of performing various
compute functions described below. In some embodiments, the compute
engine 1302 may be embodied as a single device such as an
integrated circuit, an embedded system, a field-programmable gate
array (FPGA), a system-on-a-chip (SOC), or other integrated system
or device. In the illustrative embodiment, the compute engine 1302
includes or is embodied as a storage drive controller 1304 (similar
to the storage drive controller 1214) and a memory 1306. The
storage drive controller 1304 may be embodied as any type of
processor capable of performing the functions described herein. For
example, the storage drive controller 1304 may be embodied as a
microcontroller, a single or multi-core processor(s), or other
processor or processing/controlling circuit. In some embodiments,
the storage drive controller 1304 may be embodied as, include, or
be coupled to an FPGA, an application specific integrated circuit
(ASIC), reconfigurable hardware or hardware circuitry, or other
specialized hardware to facilitate performance of the functions
described herein. In the illustrative embodiment, the storage drive
controller 1304 includes a storage health unit 1216 which may be
embodied as any device or circuitry (e.g., a co-processor, an ASIC,
etc.) capable of performing the storage discovery methods described
herein.
[0054] The memory 1306 may be embodied as any type of volatile
(e.g., dynamic random access memory (DRAM), etc.) or non-volatile
memory or data storage capable of performing the functions
described herein. Volatile memory may be a storage medium that
requires power to maintain the state of data stored by the medium.
Non-limiting examples of volatile memory may include various types
of random access memory (RAM), such as dynamic random access memory
(DRAM) or static random access memory (SRAM). One particular type
of DRAM that may be used in a memory module is synchronous dynamic
random access memory (SDRAM). In particular embodiments, DRAM of a
memory component may comply with a standard promulgated by JEDEC,
such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F
for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR
(LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4
for LPDDR4 (these standards are available at www.jedec.org). Such
standards (and similar standards) may be referred to as DDR-based
standards and communication interfaces of the storage devices that
implement such standards may be referred to as DDR-based
interfaces.
[0055] In one embodiment, the memory device is a block addressable
memory device, such as those based on NAND or NOR technologies. A
memory device may also include future generation nonvolatile
devices, such as a three dimensional crosspoint memory device
(e.g., Intel 3D XPoint.TM. memory), or other byte addressable
write-in-place nonvolatile memory devices. In one embodiment, the
memory device may be or may include memory devices that use
chalcogenide glass, multi-threshold level NAND flash memory, NOR
flash memory, single or multi-level Phase Change Memory (PCM), a
resistive memory, nanowire memory, ferroelectric transistor random
access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive
random access memory (MRAM) memory that incorporates memristor
technology, resistive memory including the metal oxide base, the
oxygen vacancy base and the conductive bridge Random Access Memory
(CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magnetic
junction memory based device, a magnetic tunneling junction (MTJ)
based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer)
based device, a thyristor based memory device, or a combination of
any of the above, or other memory. The memory device may refer to
the die itself and/or to a packaged memory product.
[0056] In some embodiments, 3D crosspoint memory (e.g., Intel 3D
XPoint.TM. memory) may comprise a transistor-less stackable cross
point architecture in which memory cells sit at the intersection of
word lines and bit lines and are individually addressable and in
which bit storage is based on a change in bulk resistance. In some
embodiments, all or a portion of the memory 1306 may be integrated
into the storage drive controller 1304. In operation, the memory
1306 may store various software and data used during operation such
as performance data, provisionable capacity data, workload data,
applications, programs, and libraries.
[0057] The compute engine 1302 is communicatively coupled to other
components of the storage device 1230 via the I/O subsystem 1308,
which may be embodied as circuitry and/or components to facilitate
input/output operations with the compute engine 1302 (e.g., with
the storage drive controller 1304 and/or the memory 1306) and other
components of the storage device 1230. For example, the I/O
subsystem 1308 may be embodied as, or otherwise include, memory
controller hubs, input/output control hubs, integrated sensor hubs,
firmware devices, communication links (e.g., point-to-point links,
bus links, wires, cables, light guides, printed circuit board
traces, etc.), and/or other components and subsystems to facilitate
the input/output operations. In some embodiments, the I/O subsystem
1308 may form a portion of a system-on-a-chip (SoC) and be
incorporated, along with one or more of the storage drive
controller 1304, the memory 1306, and other components of the
storage device 1230, into the compute engine 1302.
[0058] The communication circuitry 1310 may be embodied as any
communication circuit, device, or collection thereof, capable of
enabling communications over the network 1290 between the storage
device 1230 and another compute device (e.g., the storage sled
1210, the orchestrator server 1202). The communication circuitry
1310 may be configured to use any one or more communication
technology (e.g., wired or wireless communications) and associated
protocols (e.g., Ethernet, Bluetooth.RTM., Wi-Fi.RTM., WiMAX, etc.)
to effect such communication.
[0059] The communication circuitry 1310 may include a network
interface controller (NIC) 1314 (e.g., as an add-in device), which
may also be referred to as a host fabric interface (HFI). The NIC
1312 may be embodied as one or more add-in-boards, daughter cards,
network interface cards, controller chips, chipsets, or other
devices that may be used by the storage device 1230 to connect with
another compute device (e.g., the compute sled 1204), the
orchestrator server 1202, etc.). In some embodiments, the NIC 1312
may be embodied as part of a system-on-a-chip (SoC) that includes
one or more processors, or included on a multichip package that
also contains one or more processors. In some embodiments, the NIC
1312 may include a local processor (not shown) and/or a local
memory (not shown) that are both local to the NIC 1312. In such
embodiments, the local processor of the NIC 1312 may be capable of
performing one or more of the functions of the compute engine 1302
described herein. Additionally or alternatively, in such
embodiments, the local memory of the NIC 1312 may be integrated
into one or more components of the storage device 1230 at the board
level, socket level, chip level, and/or other levels. In the
illustrative embodiment, the storage device 1230 accesses the
network 1290 through a local bus (e.g., a peripheral component
interconnect express bus (PCIe)) that is connected to one or more
network interface controllers on the corresponding data storage
sled 1210.
[0060] The storage device 1230 may also include one or more storage
units 1218, which may be embodied as any type of devices configured
for short-term or long-term storage of data such as, for example,
memory devices and circuits, memory cards, hard disk drives,
solid-state drives, or other data storage devices. Each storage
unit 1218 may include a system partition that stores data and
firmware code for the storage unit 1218. Each storage unit 1218 may
also include one or more operating system partitions that store
data files and executables for operating systems. As described
above with respect to FIG. 12, each storage unit 1218 is
partitioned into provisionable space 1222 and usable storage
capacity 1224.
[0061] The orchestrator server 1202, the storage sleds 1210, 1220,
and the compute sled 1204 may have components similar to those
described in FIG. 13. Further, it should be appreciated that any of
the storage device 1230, the storage sleds 1210, 1220, the
orchestrator server 1202, or the compute sled 1204 may include
other components, sub-components, and devices commonly found in a
computing device, which are not discussed above in reference to the
storage device 1230 and not discussed herein for clarity of the
description.
[0062] As described above, the orchestrator server 1202, the sleds
1210, 1220, and the compute sled 1204 are illustratively in
communication via the network 1290, which may be embodied as any
type of wired or wireless communication network, including global
networks (e.g., the Internet), local area networks (LANs) or wide
area networks (WANs), cellular networks (e.g., Global System for
Mobile Communications (GSM), 3G, Long Term Evolution (LTE),
Worldwide Interoperability for Microwave Access (WiMAX), etc.),
digital subscriber line (DSL) networks, cable networks (e.g.,
coaxial networks, fiber networks, etc.), or any combination
thereof.
[0063] Referring now to FIG. 14, the orchestrator server 1202 may
establish an environment 1400 during operation. The illustrative
environment 1400 includes a network communicator 1420 and a storage
device allocation manager 1430. Each of the components of the
environment 1400 may be embodied as hardware, firmware, software,
or a combination thereof. As such, in some embodiments, one or more
of the components of the environment 1400 may be embodied as
circuitry or a collection of electrical devices (e.g., network
communicator circuitry 1420, storage device allocation circuitry
1430, etc.). In the illustrative embodiment, the environment 1400
includes storage device performance data 1404 which may be embodied
as any data indicative of statistics, metrics, scores, reports, or
the like, pertaining to the performance of one or more storage
devices 1230. For example, the storage device performance data 1404
may be embodied as any data indicative of latency,
drive-writes-per-day (DWPD), throughput, input-output operations
per second (IOPS), application workload data, or the like.
[0064] As used herein, drive writes per day refers to an amount of
data that can be written to the storage unit 1218 per day. For
example, if a storage unit 1218 is a 100 GB solid state drive and
is specified for 1 DWPD, it can withstand 100 GB of data written to
it every day for the warranty period (e.g., a period of time, such
as three years, that is defined within a performance specification
and/or service-level agreement for the storage unit 1218).
Alternatively, if a 100 GB SSD is specified for 10 DWPD, it can
withstand 1TB of data written to it every day for the warranty
period. In addition, the storage device performance data 1404 may
include values for the abovementioned data as reported by a storage
device (e.g., the storage device 1230) as well as projected,
anticipated, expected, or required values for the abovementioned
data. For example, storage device performance data 1404 may include
a latency threshold value that a storage device 1230 is expected to
meet (e.g., pursuant to a service-level agreement).
[0065] Additionally, the environment 1400 includes write-intensive
storage devices data 1404 and read-intensive storage devices data
1406. Write-intensive storage devices data 1404 may be embodied as
any data indicative of identifiers and other data for storage
devices that are configured for write-intensive operations and/or
have been assigned to a write-intensive storage device pool. As
used herein, a write-intensive storage device refers to a storage
device 1230 that is configured for a high degree of write
operations. For example, a write-intensive storage device is
configured for a relatively high number of drive writes per day.
Read-intensive storage devices data 1404 may be embodied as any
data indicative of identifiers and other data for storage devices
that are configured for read-intensive operations and/or have been
assigned to a read-intensive storage device pool. As used herein, a
read-intensive storage device refers to a storage device 1230 that
is configured for a lower degree of write operations. In other
words, a read-intensive storage device is mostly used for reading
or accessing data. It is to be understood that write-intensive
operations cause wear-out and failure on a storage device after a
period of time. By contrast, simply reading data from a storage
device causes much less impact on the underlying storage units 1218
and may enable the storage device 1230 to be used for longer than
its warranty period.
[0066] In the illustrative environment 1400, the network
communicator 1420, which may be embodied as hardware, firmware,
software, virtualized hardware, emulated architecture, and/or a
combination thereof as discussed above, is configured to facilitate
inbound and outbound network communications (e.g., network traffic,
network packets, network flows, etc.) to and from the storage
device 1230, respectively. To do so, the network communicator 1420
is configured to receive and process data packets from one system
or computing device (e.g., the storage sled 1210, the compute sled
1204, etc.) and to prepare and send data packets to a computing
device or system (e.g., the storage sled 1210, the compute sled
1204, etc.). Accordingly, in some embodiments, at least a portion
of the functionality of the network communicator 1420 may be
performed by the communication circuitry 1310, and, in the
illustrative embodiment, by the NIC 1312.
[0067] The storage device allocation manager 1430, which may be
embodied as hardware, firmware, software, virtualized hardware,
emulated architecture, and/or a combination thereof, is configured
to determine the health and performance of a connected storage
device (e.g., the storage device 1230). If the determined
performance fails to satisfy one or more performance thresholds,
the storage device allocation manager 1430 is configured to cause
the storage device 1230 to adjust its operation in one or more
ways. For example, the storage device allocation manager 1430 may
cause the storage device 1230 to adjust a performance parameter, or
reallocate the storage device 1230 from one storage device pool to
another (e.g., from a write-intensive pool to a read-intensive
pool).
[0068] In the illustrative embodiment, the storage device
allocation manager 1430 receives storage device data from the
storage device 1230. The storage device data includes results from
a storage device self-test. The storage device self-test is
performed by the storage device 1230 to test one or more storage
device performance parameters (e.g., latency, IOPS, throughput,
bandwidth, response time, application workload, or the like). Each
storage device performance parameter may adhere to specific
threshold values (e.g., pursuant to a service-level agreement may
define a performance specification for the storage device 1230).
For example, the performance specification may state performance
targets, including that the storage device 1230 is configured for a
particular write latency (e.g., 80 microseconds). Write latency
refers to the elapsed time to execute a data write operation.
Accordingly, the storage device 1230 measures its own write latency
at certain times. The storage device 1230 may also determine
whether the measured write latency meets the threshold defined by
the performance specification. The outcome is recorded in a
self-test result and submitted to the storage device allocation
manager 1430.
[0069] If the storage device 1230 satisfies performance thresholds,
based on the self-test results, the storage device allocation
manager 1430 may determine that the storage device is performing
according to specifications. Otherwise (e.g., if the storage device
1230 does not satisfy the performance thresholds), the storage
device allocation manager 1430 may generate an adjustment message
for the storage device 1230. The adjustment message includes data
to cause the storage device 1230 to adjust at least one storage
device performance parameter. For example, the adjustment message
may include a request to adjust power supply to a component of the
storage device 1230 in order to reduce the write latency down to
specified thresholds. As another example, the adjustment message
may cause the storage device 1230 to select a faster connection to
another compute device (e.g., the compute sled 1204) in order to
reduce the write latency. As yet another example, the adjustment
message may reduce an application workload for the storage device
1230 in order that the write latency for the remaining application
workload satisfies performance thresholds.
[0070] In the illustrative embodiment, the storage device
allocation manager 1430 determines whether a storage device 1230
should be reallocated to a different storage device pool. For
example, the storage device 1230 may presently be in a
write-intensive storage device pool. However, the storage device
allocation manager 1430 may determine, based on one or more
self-test results, that the storage device 1230 is unable to
provide the write latency specified in the performance
specification. The storage device allocation manager 1430 may
arrive at this determination after performing one or more
preliminary adjustments. For example, the storage device allocation
manager 1430 may first adjust other storage device performance
parameters (e.g., power, network connectivity) and transmit a
request to the storage device 1230 to repeat the self-test. If the
self-test results still fail to satisfy performance thresholds, the
storage device allocation manager 1430 may determine to reallocate
the storage device 1230 to a different storage device pool. As
another example, the storage device allocation manager 1430 may
immediately reallocate the storage device 1230 to a different
storage device pool without first performing any preliminary
adjustments.
[0071] In the illustrative embodiment, the storage device
allocation manager 1430 may reallocate the storage device 1230 from
a write-intensive storage device pool to a read-intensive storage
device pool. In doing so, the storage device allocation manager
1430 may identify a storage device pool where the performance
levels required of the storage devices 1230 match the performance
of the storage device 1230. In the illustrative embodiment, the
target storage device pool is a more read-intensive storage device
pool. For example, a higher write latency may be tolerable in the
target device pool, thus making it a more read-intensive storage
device pool than the source device pool.
[0072] In another embodiment, the storage device allocation manager
1430 may determine, based on self-test results, whether the storage
device 1230 is reaching a critical level of drive writes per day
(DWPD). For example, a DWPD value of 1 may be specified for the
storage device 1230 for a period of one year or 365 calendar days.
The storage device allocation manager 1430 may determine that the
storage device 1230 has handled at least one DWPD for the past 350
days. Accordingly, the storage device allocation manager 1430 may
determine that write performance for the storage device 1230 will
soon degrade if it remains in its present device pool. In other
words, the storage device allocation manager 1430 may determine
that the present device pool of the storage device 1230 will soon
be too write-intensive for the storage device 1230. Continuing the
example, the storage device allocation manager 1430 may then
reallocate the storage device 1230 to a storage device pool where
the DWPD level is lower, thereby extending the usable life of the
storage device 1230.
[0073] Referring now to FIG. 15, the storage device 1230 may
establish an environment 1500 during operation. The illustrative
environment 1500 includes a network communicator 1520 and a storage
health manager 1530. The storage health manager 1530, in the
illustrative embodiment, includes a storage capacity allocator
1532, a performance estimator 1534, a self-test execution manager
1536, and a data input/output manager 1538. Each of the components
of the environment 1500 may be embodied as hardware, firmware,
software, or a combination thereof. As such, in some embodiments,
one or more of the components of the environment 1500 may be
embodied as circuitry or a collection of electrical devices (e.g.,
network communicator circuitry 1520, storage health manager
circuitry 1530, storage capacity allocator circuitry 1532,
performance estimator circuitry 1534, self-test execution manager
circuitry 1536, and data input/output manager 1538 circuitry,
etc.).
[0074] In the illustrative embodiment, the environment 1500
includes performance data 1504, capacity data 1506, and workload
data 1508. Performance data may be embodied as any data indicative
of statistics, metrics, scores, reports or the like pertaining to a
storage device's performance For example, performance data may
include the warranty period for the storage device 1230, DWPD
values for the storage device 1230, the mean time between failures
(MTBF) values, uncorrectable bit error rate (UBER) values, or the
like. The capacity data 1506 may be embodied as any data indicative
of the provisionable and usable capacity (e.g., the provisionable
space 1222 and the usable storage capacity 1224) of the storage
units 1218 that are included within the storage device 1230. The
capacity data 1506 may also include capacity provisioning ratio
values for each storage unit 1218 within the storage device 1230.
The capacity provisioning ratio may be embodied as the proportion
of a storage unit 1218 that is usable storage space (e.g., usable
storage capacity 1224) as opposed to provisionable space 1222. This
capacity provisioning ratio may be expressed within the storage
device 1230 as one or more of a percentage, a ratio, a set of
locators, sectors, or cells assigned to be usable versus
provisionable, or the like. For example, a storage unit 1218 of
total capacity 100 GB may be configured such that 80 GB is usable
storage space and 20 GB is provisionable space. Accordingly, the
capacity provisioning ratio may be expressed as 80%, 20%, 4:1, the
address ranges assigned to each type of space, or the like. The
workload data 1508 may be embodied as any data indicative of
application data stored in the storage device 1230, data indicative
of applications that store data on the storage device 1230, and/or
identifiers of the compute sleds or compute devices to which the
storage device 1230 provides storage services.
[0075] The network communicator 1520 is similar to the network
communicator 1420 described above with reference to FIG. 14. The
storage health manager 1530, in the illustrative embodiment, is
configured to allocate storage within the data storage unit(s)
1218, perform a self test of the performance of the data storage
unit(s) 1218 relative to one or more performance thresholds, and
manage access to the data stored in the data storage unit(s) 1218
(e.g., in response to access requests from remote compute
device(s), such as the compute sled 1204). The storage capacity
allocator 1532, in the illustrative embodiment, is configured to
allocate storage capacity within the storage device 1230 (e.g.,
within the storage unit(s) 1218). For example, a storage device
1230, on initial startup, may receive a capacity provisioning ratio
from the orchestrator server 1202. In response, the storage device
1230 may allocate space within the storage unit(s) 1218 according
to the capacity provisioning ratio.
[0076] The performance estimator 1534 is configured to calculate
and return performance values for a storage unit 1218 within the
storage device 1230. Initially, the performance estimator 1534 may
be programmed with performance specification data for a storage
unit 1218. Performance specification data may be obtained from
documentation or a local or remote database. Using the performance
specification data, the performance estimator 1534 may determine
initial values for one or more performance parameters for the
storage unit 1218. For example, the performance estimator 1534 may
determine, for the storage unit 1218, a DWPD value, latency values,
reliability values, temperature values, bandwidth values,
throughput values, or the like. As the storage unit 1218 operates,
the performance estimator 1534 may calculate performance data such
as a present DWPD value for the storage unit 1218, return a
remaining warranty period for the storage unit 1218, return a
provisionable space value and/or return a remaining number of
unused storage cells for the storage unit 1218. The performance
estimator 1534 is also configured to return a specific identifier
or metric to represent a remaining lifespan of a storage unit 1218.
The metric is may be a function of various performance data values
that are described above with respect to performance data 1504.
[0077] The self-test execution manager 1536 is configured to
receive a self-test from the orchestrator server 1202, perform the
self-test with respect to one or more storage units 1218 of the
storage device 1230, and report results to the orchestrator server
1202. In the illustrative embodiment, the storage device allocation
manager 1430 of the orchestrator server 1202 analyzes the
performance values generated by the performance estimator 1534 to
generate a self-test routine tailored to the specifications of a
particular storage unit 1218. The orchestrator server 1202 may send
the self-test routine to the storage device 1230, which executes
the self-test with the self-test execution manager 1536. The
self-test execution manager 1536, in the illustrative embodiment,
collects and reports the results of the self-test to the
orchestrator server 1202.
[0078] The data input/output (I/O) manager 1538 is configured to
manage inputs and outputs from and to other compute devices
respectively. For example, the data I/O manager 1538 manages inputs
from an orchestrator server 1202 such as self-tests or from a
compute sled 1204 such as application data. As another example, the
data I/O manager 1538 manages outputs to the orchestrator server
1202 such as self-test results or to the compute sled 1204 such as
application data requested by an application to be retrieved from
storage (e.g., from workload data 1508).
[0079] It should be appreciated that each of the storage health
manager 1530, the storage capacity allocator 1532, the performance
estimator 1534, the self-test execution manager 1536, and the data
input/output manager 1538 may be separately embodied as hardware,
firmware, software, virtualized hardware, emulated architecture,
and/or a combination thereof. For example, the storage health
manager 1530 may be embodied as a hardware component, while the
storage capacity allocator 1532, the performance estimator 1534,
the self-test execution manager 1536, and the data input/output
manager 1538 are embodied as virtualized hardware components or as
some other combination of hardware, firmware, software, virtualized
hardware, emulated architecture, and/or a combination thereof.
[0080] Referring now to FIG. 16, the storage device 1230, in
operation, may execute a method 1600 for storage discovery and
automatic repurposing of storage devices. The method 1600 begins
with block 1602, in which the storage device 1230 determines to
perform the storage discovery methods described herein. In block
1604, the storage device 1230 obtains storage drive performance
specification data. As described above with respect to FIG. 15, the
performance estimator 1534 may access performance specification
data. As an example, and as illustrated in block 1606, the storage
device 1230 may read performance specification data from
non-volatile memory in the storage device 1230. For example,
performance specification data may have been stored at an earlier
time in a storage unit 1218. Alternatively, and as illustrated in
block 1608, the storage device 1230 may electronically read and
parse performance specification documents received from an external
database, or read in via an input/output device (e.g., a scanner).
In block 1610, the storage device 1230 may store the performance
specification data in the storage device 1230 (e.g., if not already
stored). In block 1612, the storage device 1230 sends the
performance specification data to the orchestrator server 1202
(e.g., through the network 1290).
[0081] In block 1614, the storage device 1230 may determine
testable performance specification values. Irrespective of a
self-test routine to be later received from an orchestrator server
1202, the storage device 1230 may be identify and distinguish
testable performance specification values from non-testable values
(e.g., drive form factor, weight, size specifications, etc.). More
specifically, as shown in block 1616, the storage device 1230
retrieves expected performance specification values from, for
example, a performance specification document or service-level
agreement.
[0082] In block 1618, the storage device 1230 performs a self-test
of one or more storage units 1218. In the illustrative embodiment,
a self-test is designed to measure the performance of the storage
unit 1218 using one or more performance parameters (e.g., latency,
IOPS, throughput, etc.). As described above, the orchestrator
server 1202 may generate a self-test routine tailored to the
storage device 1230 and/or a storage unit 1218 and transmit the
self-test routine to the storage device 1230. In another
embodiment, the storage device 1230 is configured with a self-test
that is programmed within the storage drive controller 1304. As
illustrated in block 1620, the storage device 1230 may test the
IOPS values for a storage unit 1218 of the data storage device
1230. As another example, shown in block 1622, the storage device
1230 may test drive latency for a storage unit 1218 (e.g., by
measuring the time taken to write a test data set to the data
storage unit 1218 the data to be written).
[0083] As yet another example, in block 1624, the storage device
1230 may determines the current DWPD value. More specifically, the
current DWPD value may be a calculation of the total drive writes
to the storage unit 1218 (e.g., in gigabytes) divided by the total
number of days of operation. The storage device 1230 may also
determine the possible future drive writes per day value (e.g., by
identifying a historical trend in DWPD over multiple days and
projecting the trend out to a particular date), as indicated in
block 1626. As still another example, shown in block 1628, the
storage device 1230 determines a temperature of the storage unit
1230. As a still further example, shown in block 1630, the storage
device 1230 determines network metrics of the storage unit 1230
(such as throughput, bandwidth, etc.). In block 1632, the storage
device 1230 reports results of the self-test to the orchestrator
server 1202. In some embodiments, in reporting the self-test
results, the storage device 1230 sends the values determined in one
or more of the blocks 1620, 1622, 1624, 1626, 1628, 1630 to the
orchestrator server 1202. In other embodiments, the storage device
1230 sends a result of a comparison of one or more of the values
determined in blocks 1620, 1622, 1624, 1626, 1628, 1630 to a
corresponding target value (e.g., a value defined in the
specification data, such as a target latency or IOPS that is to be
satisfied). For example the storage device 1230 may send an
indication that the measured IOPS is 95% of the target IOPS defined
in the specification data or that the measured latency is 103% of
the latency defined in the specification data.
[0084] Referring now to FIG. 17, in block 1634, the storage device
1230 determines whether an adjustment message was received from the
orchestrator server 1202. If no adjustment message is received, the
method loops back to block 1618, where the storage device 1230
continues to perform self-testing (e.g., on a predetermined
schedule). If an adjustment message is received, the method 1600
branches to block 1636. In block 1636, the storage device 1230
implements the adjustment instructions included within the
adjustment message. For example, and as shown in block 1638, the
storage device 1230 adjusts the usable storage space of a storage
unit 1218. In the illustrative embodiment, the sum of usable
storage space and provisionable space does not exceed the total
available space within a storage unit 1218. Accordingly, the
adjustment instruction may be to reconfigure the storage unit 1218
with more usable storage space (and thus less provisionable space),
thereby rendering the storage unit 1218 relatively more
read-intensive than before. As noted earlier, this is because a
reduction in provisionable space reduces a number of available
unused storage cells that can replace aged or worn-out cells within
the usable storage space, thereby reducing the number of times the
storage unit 1218 can be written to. However, reducing
provisionable space does not affect the ability of a storage unit
1218 to fulfill data read requests. By contrast, the adjustment
instruction may be to reconfigure the storage unit 1218 with less
usable storage space (and thus more provisionable space), and make
the storage unit 1218 relatively more write-intensive. Similarly,
and as shown in block 1640, the storage device 1230 may adjust the
provisionable space of the storage unit 1218 in response to the
adjustment instruction. Increasing the provisionable space will
bring about a reduction in usable storage space, and vice
versa.
[0085] As another example of adjustment, shown in block 1642, the
storage device 1230 may change its logical connection with one or
more compute devices (e.g., to discontinue data access services for
a sled (e.g., a compute sled) executing a workload (e.g., an
application) that is write-intensive and begin data access services
for a sled executing a workload that is more read-intensive than
write intensive). For instance, the adjustment may be to have the
storage device 1230 discontinue data access operations for a
compute sled or accelerator sled whose performance requirements are
no longer being met by the storage device 1230. The adjustment
instruction may be to increase or decrease a connectivity speed
(e.g., by using a different port or connection methodology and/or
allocating a larger or smaller portion of the total communication
bandwidth to servicing data access requests from a particular
sled). As yet another example of adjustment, shown in block 1644,
the storage device 1230 updates one or more configuration values in
response to a new allocation by the orchestrator server 1202. The
adjustment message includes information on the reallocation. For
example, the adjustment message may inform the storage device 1230
that it has been reassigned to a different storage device pool
(e.g., a read-intensive storage device pool).
[0086] Referring now to FIG. 18, the orchestrator server 1202 may
execute a method 1800 for storage discovery and automatic
repurposing of storage devices. In block 1802, the orchestrator
server 1202 determines whether to monitor a storage device (e.g., a
storage device 1230). If so, the method branches to block 1804,
where the orchestrator server 1202 sets a capacity provisioning
ratio for the storage device 1230. As described above, the capacity
provisioning ratio refers to the proportion of a storage unit 1218
that is usable storage space as opposed to provisionable space.
This capacity provisioning ratio may be expressed within the
storage device 1230 as one or more of a percentage, a ratio, a set
of locators, sectors, cells assigned to be usable versus
provisionable, or the like. The orchestrator server 1202 sets the
capacity provisioning ratio as a function of a number of variables,
including, for example, the anticipated pool assignment (e.g., to a
write-intensive pool or to a read-intensive pool) for the storage
device 1230, the expected performance requirements of other compute
devices that will request data access services from the storage
device 1230, the reported performance specifications of the storage
device 1230, and the like. In block 1806, the orchestrator server
1202 transmits the capacity provisioning ratio to the storage
device 1230.
[0087] In block 1808, the orchestrator server 1202 monitors storage
device self-test results. As described above with respect to block
1632 of FIG. 16, the storage device 1230 sends self-test results to
the orchestrator server 1202. In the illustrative embodiment, the
self-tests may be performed periodically, on demand by the
orchestrator server 1202, in response to a stimulus by the storage
device 1230 or other connected compute device, or in response to an
event (e.g., initial startup, boot time, an error, a failure, a
power surge, an unexpected data read or write event, etc.). In
block 1810, the orchestrator server 1202 analyzes the received
self-test results and determines if the storage device 1230 is
meeting one or more performance targets or thresholds (e.g.,
pursuant to performance specification data, pursuant to a service
level agreement, etc.). Accordingly, and as shown in block 1812,
the orchestrator server 1202 may query compute devices connected to
or reliant on the storage device 1230 (e.g., to the storage sled
1210, 1220 in which the storage device 1230 is located) for
performance targets or may access service-level agreement
documentation for performance target data.
[0088] The method advances to block 1814, where the orchestrator
server 1202 compares self-test results received from the storage
device 1230 to the service-level agreement targets and also any
other performance targets received from compute devices that have
been assigned to use the storage device 1230. As an example, in
block 1816, the orchestrator server 1202 compares latency values
from the self-test to latency targets. More specifically, the
orchestrator server 1202 compares, for example, a write latency
value returned from the storage device 1230 to write latency
targets provided from performance specification data within the
service-level agreement and/or write latency requirements requested
by individual compute sleds, accelerator sleds, or the like. As
another example, in block 1818, the orchestrator server 1202
compares IOPS values to IOPS targets. As yet another example, in
block 1820, the orchestrator server 1202 compares drive writes per
day values to present drive writes per day targets. As a still
further example, in block 1822, the orchestrator server 1202
compares other health metrics (e.g., network connectivity speeds,
temperature, noise levels, etc.).
[0089] In block 1824, the orchestrator server 1202 determines
whether the self-test results meet the expected or required
performance levels. If the results meet expectations, the method
loops back to block 1802 and continues to monitor the storage
device 1230. If the results do not meet expectations, the method
branches to block 1826 of FIG. 19. Referring now to FIG. 19, in
block 1826, the orchestrator server 1202 determines an adjustment
for the storage device 1230. For example, the orchestrator server
1202 determines a type of adjustment (e.g., reallocation,
configuration change, update connections to compute devices (e.g.,
sleds) that are to remotely access the storage device 1230, etc.)
or a degree of adjustment (adjustment to capacity provisioning
ratio, etc.).
[0090] For example, and as illustrated in block 1828, the
orchestrator server 1202 determines whether the capacity
provisioning ratio assigned to the storage device 1230 needs to be
updated. The capacity provisioning ratio may have been assigned to
the entire storage device 1230 or to individual storage units 1218
of the storage device 1230. As described above, in response to a
determination that the write latency of a storage device 1230 is
increasing, the orchestrator server 1202 may determine that the
write performance of the storage device 1230 is declining.
Accordingly, the orchestrator server 1202 may determine to increase
the provisionable space for one or more storage units 1218 of the
storage device 1230 in order to make available greater numbers or
fresh storage cells that can more quickly replace aged storage
cells in the usable space. The orchestrator server 1202 may
determine that such an adjustment to the capacity provisioning
ratio will bring the write latency of the storage device 1230
closer to expected performance levels.
[0091] As another example, in block 1830, the orchestrator server
1202 determines whether a drive writes per day value exceeds a
threshold. If the DWPD value exceeds a performance threshold (e.g.,
a threshold based on the age of the storage device 1230), the
orchestrator server 1202 may determine that the storage device 1230
will soon (e.g., within a predefined number of days) reach an end
of life and become unusable at least as a write-intensive storage
device. As a result of the determinations in block 1826, the
orchestrator server 1202 generates an adjustment message in block
1832. More specifically, in block 1834, the orchestrator server
1202 may set an updated capacity provisioning ratio for the storage
device 1230 as described above. As another example, and as
illustrated in block 1836, the orchestrator server 1202 may update
the storage device allocation. More specifically, the storage
device 1230 may be reallocated to a different storage device pool
(e.g., to a more read-intensive storage device pool or to a more
write-intensive storage device pool). The storage device 1230 may
also be decommissioned. As illustrated in block 1838, the
orchestrator server 1202 may implement a progressive or tiered
reallocation of the storage device 1230. For example, the
orchestrator server 1202 may execute a planned de-escalation of
write intensity for the storage device 1230. In doing so, the
orchestrator server 1202 may continually reassign the storage
device 1230 to progressively more read-intensive storage device
pools (e.g., on a predetermined schedule). Doing so may extend the
usable lifetime of the storage device 1230.
[0092] As another example, shown in block 1840, the orchestrator
server 1202 may adjust other storage device configurations. For
example, the orchestrator server 1202 may adjust execution times of
certain types of software (e.g., security software in the storage
device 1230 that may reduce performance) to increase the
availability of the storage device 1230 to respond to data access
requests from other sleds (e.g., from the compute sled 1204) during
certain times of day. The orchestrator server 1202 may adjust a
boot schedule for the storage device 1230. Additionally or
alternatively, the orchestrator server 1202 may adjust a
connectivity or component usage for the storage device 1230, as
another example. In block 1842, the orchestrator server 1202
transmits the adjustment message bearing one or more of the
aforementioned adjustment instructions to the storage device 1230.
As described above with respect to block 1636, this causes the
storage device 1230 to implement the one or more adjustment
instructions.
EXAMPLES
[0093] Illustrative examples of the technologies disclosed herein
are provided below. An embodiment of the technologies may include
any one or more, and any combination of, the examples described
below.
[0094] Example 1 includes a compute device to manage storage device
performance, the compute device comprising a compute engine to
receive, from a data storage sled, storage device data from a
storage device located on the data storage sled, wherein the
storage device data includes storage device self-test data that
defines a result of a self-test performed by the storage device;
determine, in response to the storage device self-test data,
whether the storage device fails to satisfy a performance
threshold; generate, in response to a determination that the
storage device fails to satisfy the performance threshold, an
adjustment message for the storage device, wherein the adjustment
message instructs the storage device to adjust a performance
parameter of the storage device; and send the adjustment message to
the storage device.
[0095] Example 2 includes the subject matter of Example 1, and
wherein to generate the adjustment message comprises to generate an
adjustment message that instructs the storage device to adjust a
capacity provisioning ratio for the storage device, wherein the
capacity provisioning ratio defines a ratio of usable storage space
to provisionable space for the storage device.
[0096] Example 3 includes the subject matter of any of Examples 1
and 2, and wherein the compute engine is to set the capacity
provisioning ratio for the storage device, and wherein the compute
engine is further to receive performance specification data from
the storage device on a first startup of the storage device,
wherein the performance specification data includes a performance
target for the storage device; generate the capacity provisioning
ratio for the storage device as a function of at least one of an
anticipated device pool allocation for the storage device and the
performance target; and send the capacity provisioning ratio to the
storage device to cause the storage device to apportion a usable
storage space and a provisionable space within a storage unit of
the storage device as a function of the capacity provisioning
ratio.
[0097] Example 4 includes the subject matter of any of Examples
1-3, and wherein the storage device is associated with a first
device pool, and wherein the compute engine is further to
determine, in response to receipt of the storage device self-test
data, whether the storage device fails to satisfy a performance
threshold for the first device pool; determine whether the storage
device satisfies a performance threshold for a second device pool,
different from the first device pool; and reallocate, in response
to to determination that the storage device satisfies the
performance threshold for the second device pool, the storage
device to the second device pool.
[0098] Example 5 includes the subject matter of any of Examples
1-4, and wherein to generate the adjustment message comprises to
generate an adjustment message to reallocate the storage device
from a write-intensive storage device pool to a read-intensive
storage device pool.
[0099] Example 6 includes the subject matter of any of Examples
1-5, and wherein a first drive writes per day value for the first
device pool exceeds a second drive writes per day value for the
second device pool.
[0100] Example 7 includes the subject matter of any of Examples
1-6, and wherein the storage device performance parameter includes
an identifier for a compute sled connected to the storage device,
an amount of usable storage space, an amount of provisionable
space, or a storage device configuration value.
[0101] Example 8 includes the subject matter of any of Examples
1-7, and wherein the compute engine is further to query a compute
sled connected to the storage device for a performance target of
the storage device, wherein the performance target is associated
with a workload to be executed by the compute sled; determine
whether the storage device fails to satisfy the performance target;
and generate, in response to a determination that the storage
device fails to satisfy the performance target, a second adjustment
message for the storage device, wherein the second adjustment
message instructs the storage device to satisfy the performance
target associated with the compute sled.
[0102] Example 9 includes the subject matter of any of Examples
1-8, and wherein to determine whether the storage device fails to
satisfy a performance threshold comprises to retrieve at least one
of a latency value, an input-output per second (IOPS) value, or a
drive writes per day (DWPD) value from the storage device self-test
data; determine, as a function of performance specification data
that includes a performance target for the storage device, at least
one of a latency threshold, an IOPS threshold, or a DWPD threshold;
and compare one or more of the latency value to the latency
threshold, the IOPS value to the IOPS threshold, or the DWPD value
to the DWPD threshold.
[0103] Example 10 includes the subject matter of any of Examples
1-9, and wherein the compute engine is further to identify the one
or more performance thresholds based on performance specification
data that includes a performance target for the storage device;
generate, as a function of the one or more performance thresholds,
a self-test for the storage device to perform; and transmit the
self-test to the storage device.
[0104] Example 11 includes the subject matter of any of Examples
1-10, and wherein the compute engine is to compose a managed node
that includes the storage device.
[0105] Example 12 includes a method to manage storage device
performance, the method comprising receive, from a data storage
sled, storage device data from a storage device located on the data
storage sled, wherein the storage device data includes storage
device self-test data that defines a result of a self-test
performed by the storage device; determine, in response to the
storage device self-test data, whether the storage device fails to
satisfy a performance threshold; generate, in response to a
determination that the storage device fails to satisfy the
performance threshold, an adjustment message for the storage
device, wherein the adjustment message instructs the storage device
to adjust a performance parameter of the storage device; and send
the adjustment message to the storage device.
[0106] Example 13 includes the subject matter of Example 12, and
wherein generating the adjustment message comprises generating an
adjustment message that instructs the storage device to adjust a
capacity provisioning ratio for the storage device, wherein the
capacity provisioning ratio defines a ratio of usable storage space
to provisionable space for the storage device.
[0107] Example 14 includes the subject matter of any of Examples 12
and 13, and further including receiving, by the compute device,
performance specification data from the storage device on a first
startup of the storage device, wherein the performance
specification data includes a performance target for the storage
device; generating, by the compute device, the capacity
provisioning ratio for the storage device as a function of at least
one of an anticipated device pool allocation for the storage device
and the performance target; and sending, by the compute device, the
capacity provisioning ratio to the storage device to cause the
storage device to apportion a usable storage space and a
provisionable space within a storage unit of the storage device as
a function of the capacity provisioning ratio.
[0108] Example 15 includes the subject matter of any of Examples
12-14, and wherein the storage device is associated with a first
device pool, the method further comprising determining, by the
compute device and in response to receipt of the storage device
self-test data, whether the storage device fails to satisfy a
performance threshold for the first device pool; determining, by
the compute device, whether the storage device satisfies a
performance threshold for a second device pool, different from the
first device pool; and reallocating, by the compute device and in
response to a determination that the storage device satisfies the
performance threshold for the second device pool, the storage
device to the second device pool.
[0109] Example 16 includes the subject matter of any of Examples
12-15, and wherein generating the adjustment message comprises
generating an adjustment message to reallocate the storage device
from a write-intensive storage device pool to a read-intensive
storage device pool.
[0110] Example 17 includes the subject matter of any of Examples
12-16, and wherein a first drive writes per day value for the first
device pool exceeds a second drive writes per day value for the
second device pool.
[0111] Example 18 includes the subject matter of any of Examples
12-17, and wherein the storage device performance parameter
includes an identifier for a compute sled connected to the storage
device, an amount of usable storage space, an amount of
provisionable space, or a storage device configuration value.
[0112] Example 19 includes the subject matter of any of Examples
12-18, and further including querying, by the compute device, a
compute sled connected to the storage device for a performance
target of the storage device, wherein the performance target is
associated with a workload to be executed by the compute sled;
determining, by the compute device, whether the storage device
fails to satisfy the performance target; and generating, by the
compute device and in response to a determination that the storage
device fails to satisfy the performance target, a second adjustment
message for the storage device, wherein the second adjustment
message instructs the storage device to satisfy the performance
target associated with the compute sled.
[0113] Example 20 includes the subject matter of any of Examples
12-19, and wherein determining whether the storage device fails to
satisfy a performance threshold comprises retrieving at least one
of a latency value, an input-output per second (IOPS) value, or a
drive writes per day (DWPD) value from the storage device self-test
data; determining, as a function of performance specification data
that includes a performance target for the storage device, at least
one of a latency threshold, an IOPS threshold, or a DWPD threshold;
and comparing one or more of the latency value to the latency
threshold, the IOPS value to the IOPS threshold, or the DWPD value
to the DWPD threshold.
[0114] Example 21 includes the subject matter of any of Examples
12-20, and further including identifying, by the compute device,
the one or more performance thresholds based on performance
specification data that includes a performance target for the
storage device; generating, by the compute device and as a function
of the one or more performance thresholds, a self-test for the
storage device to perform; and transmitting, by the compute device,
the self-test to the storage device.
[0115] Example 22 includes the subject matter of any of Examples
12-21, and further including composing, by the compute device, a
managed node that includes the storage device.
[0116] Example 23 includes a compute device comprising a compute
engine to perform the method of any of Examples 12-22.
[0117] Example 24 includes a compute device comprising means for
performing the method of any of Examples 12-22.
[0118] Example 25 includes one or more machine-readable storage
media comprising a plurality of instructions stored thereon that,
in response to being executed, cause a compute device to perform
the method of any of Examples 12-22.
[0119] Example 26 includes a storage device to manage storage
performance, the storage device comprising a non-volatile memory;
and a storage drive controller to perform a self-test on the
non-volatile memory to produce self-test data indicative of a
present performance of the storage device relative to a performance
target associated with specification data for the storage device;
report, through a network, the self-test data to a compute device;
receive, through the network, an adjustment message from the
compute device; and update, in response to the adjustment message,
a storage device performance parameter.
[0120] Example 27 includes the subject matter of Example 26, and
wherein to perform the self-test comprises to select at least one
storage device performance parameter for the storage device;
identify a present value for the at least one storage device
performance parameter; and generate self-test data that includes
the present value for the at least one storage device performance
parameter.
[0121] Example 28 includes the subject matter of any of Examples 26
and 27, and wherein the storage drive controller is further to
identify the performance target from the performance specification
data; compare the present performance parameter to the performance
target; determine whether the storage device fails to satisfy the
performance target; and report, to the compute device, the
determination as part of the self-test.
[0122] Example 29 includes the subject matter of any of Examples
26-28, and wherein to update the storage device performance
parameter comprises to update at least one of a usable storage
space parameter, a provisionable space parameter, or a drive writes
per day value for the storage device.
[0123] Example 30 includes the subject matter of any of Examples
26-29, and wherein to perform the self-test comprises to determine
a present number of input/output instructions per second executable
by the storage device.
[0124] Example 31 includes the subject matter of any of Examples
26-30, and wherein to perform the self-test comprises to determine
a present latency of the storage device.
[0125] Example 32 includes the subject matter of any of Examples
26-31, and wherein to perform the self-test comprises to determine,
as a function of a historical trend in drive writes per day (DWPD),
an expected number of drive writes per day (DWPD) to be performed
by the storage device; and determine a change to a present DWPD
value caused by an update to a provisionable space parameter of the
storage device.
[0126] Example 33 includes a method for managing storage
performance, the method comprising performing, by a storage device,
a self-test on non-volatile memory of the storage device to produce
self-test data indicative of a present performance of the storage
device relative to a performance target associated with
specification data for the storage device; reporting, by the
storage device and through a network, the self-test data to a
compute device; receiving, by the storage device and through the
network, an adjustment message from the compute device; and
updating, by the storage device and in response to the adjustment
message, a storage device performance parameter.
[0127] Example 34 includes the subject matter of Example 33, and
wherein performing the self-test comprises selecting at least one
storage device performance parameter for the storage device;
identifying a present value for the at least one storage device
performance parameter; and generating self-test data that includes
the present value for the at least one storage device performance
parameter.
[0128] Example 35 includes the subject matter of any of Examples 33
and 34, and further including identifying, by the storage device,
the performance target from the performance specification data;
comparing, by the storage device, the present performance parameter
to the performance target; determining, by the storage device,
whether the storage device fails to satisfy the performance target;
and reporting, by the storage device and to the compute device, the
determination as part of the self-test.
[0129] Example 36 includes the subject matter of any of Examples
33-35, and wherein updating the storage device performance
parameter comprises updating at least one of a usable storage space
parameter, a provisionable space parameter, or a drive writes per
day value for the storage device.
[0130] Example 37 includes the subject matter of any of Examples
33-36, and wherein performing the self-test comprises determining a
present number of input/output instructions per second executable
by the storage device.
[0131] Example 38 includes the subject matter of any of Examples
33-37, and wherein performing the self-test comprises determining a
present latency of the storage device.
[0132] Example 39 includes the subject matter of any of Examples
33-38, and wherein performing the self-test comprises determining,
as a function of a historical trend in drive writes per day (DWPD),
an expected number of drive writes per day (DWPD) to be performed
by the storage device; and determining a change to a present DWPD
value caused by an update to a provisionable space parameter of the
storage device.
[0133] Example 40 includes a storage device comprising a storage
drive controller to perform the method of any of Examples
33-39.
[0134] Example 41 includes a storage device comprising means for
performing the method of any of Examples 33-39.
[0135] Example 42 includes one or more machine-readable storage
media comprising a plurality of instructions stored thereon that,
in response to being executed, cause a compute device to perform
the method of any of Examples 33-39.
[0136] Example 43 includes a compute device comprising means for
receiving, from a data storage sled, storage device data from a
storage device located on the data storage sled, wherein the
storage device data includes storage device self-test data that
defines a result of a self-test performed by the storage device;
means for determining, in response to the storage device self-test
data, whether the storage device fails to satisfy a performance
threshold; means for generating, in response to a determination
that the storage device fails to satisfy the performance threshold,
an adjustment message for the storage device, wherein the
adjustment message instructs the storage device to adjust a
performance parameter of the storage device; and means for sending
the adjustment message to the storage device.
[0137] Example 44 includes the subject matter of Example 43, and
wherein the means for generating the adjustment message comprises
means for generating an adjustment message that instructs the
storage device to adjust a capacity provisioning ratio for the
storage device, wherein the capacity provisioning ratio defines a
ratio of usable storage space to provisionable space for the
storage device.
[0138] Example 45 includes the subject matter of any of Examples 43
and 44, and wherein the compute engine is to set the capacity
provisioning ratio for the storage device, the compute device
further comprising means for receiving performance specification
data from the storage device on a first startup of the storage
device, wherein the performance specification data includes a
performance target for the storage device; means for generating the
capacity provisioning ratio for the storage device as a function of
at least one of an anticipated device pool allocation for the
storage device and the performance target; and means for sending
the capacity provisioning ratio to the storage device to cause the
storage device to apportion a usable storage space and a
provisionable space within a storage unit of the storage device as
a function of the capacity provisioning ratio.
[0139] Example 46 includes the subject matter of any of Examples
43-45, and wherein the storage device is associated with a first
device pool, the compute device further comprising means for
determining, in response to receipt of the storage device self-test
data, whether the storage device fails to satisfy a performance
threshold for the first device pool; means for determining whether
the storage device satisfies a performance threshold for a second
device pool, different from the first device pool; and means for
reallocating, in response to to determination that the storage
device satisfies the performance threshold for the second device
pool, the storage device to the second device pool.
[0140] Example 47 includes the subject matter of any of Examples
43-46, and wherein the means for generating the adjustment message
comprise means for generating an adjustment message to reallocate
the storage device from a write-intensive storage device pool to a
read-intensive storage device pool.
[0141] Example 48 includes the subject matter of any of Examples
43-47, and wherein a first drive writes per day value for the first
device pool exceeds a second drive writes per day value for the
second device pool.
[0142] Example 49 includes the subject matter of any of Examples
43-48, and wherein the storage device performance parameter
includes an identifier for a compute sled connected to the storage
device, an amount of usable storage space, an amount of
provisionable space, or a storage device configuration value.
[0143] Example 50 includes the subject matter of any of Examples
43-49, and further including means for querying a compute sled
connected to the storage device for a performance target of the
storage device, wherein the performance target is associated with a
workload to be executed by the compute sled; means for determining
whether the storage device fails to satisfy the performance target;
and means for generating, in response to a determination that the
storage device fails to satisfy the performance target, a second
adjustment message for the storage device, wherein the second
adjustment message instructs the storage device to satisfy the
performance target associated with the compute sled.
[0144] Example 51 includes the subject matter of any of Examples
43-50, and wherein the means for determining whether the storage
device fails to satisfy a performance threshold comprises means for
retrieving at least one of a latency value, an input-output per
second (IOPS) value, or a drive writes per day (DWPD) value from
the storage device self-test data; means for determining, as a
function of performance specification data that includes a
performance target for the storage device, at least one of a
latency threshold, an IOPS threshold, or a DWPD threshold; and
means for comparing one or more of the latency value to the latency
threshold, the IOPS value to the IOPS threshold, or the DWPD value
to the DWPD threshold.
[0145] Example 52 includes the subject matter of any of Examples
43-51, and further including means for identifying the one or more
performance thresholds based on performance specification data that
includes a performance target for the storage device; means for
generating, as a function of the one or more performance
thresholds, a self-test for the storage device to perform; and
means for transmitting the self-test to the storage device.
[0146] Example 53 includes the subject matter of any of Examples
43-52, and further including means for composing a managed node
that includes the storage device.
[0147] Example 54 includes a storage device to manage storage
performance, the storage device comprising a non-volatile memory;
means for performing a self-test on the non-volatile memory to
produce self-test data indicative of a present performance of the
storage device relative to a performance target associated with
specification data for the storage device; means for reporting,
through a network, the self-test data to a compute device; means
for receiving, through the network, an adjustment message from the
compute device; and means for updating, in response to the
adjustment message, a storage device performance parameter.
[0148] Example 55 includes the subject matter of Example 54, and
wherein the means for performing the self-test comprises means for
selecting at least one storage device performance parameter for the
storage device; means for identifying a present value for the at
least one storage device performance parameter; and means for
generating self-test data that includes the present value for the
at least one storage device performance parameter.
[0149] Example 56 includes the subject matter of any of Examples 54
and 55, and further including means for identifying the performance
target from the performance specification data; means for comparing
the present performance parameter to the performance target; means
for determining whether the storage device fails to satisfy the
performance target; and means for reporting, to the compute device,
the determination as part of the self-test.
[0150] Example 57 includes the subject matter of any of Examples
54-56, and wherein the means for updating the storage device
performance parameter comprises means for updating at least one of
a usable storage space parameter, a provisionable space parameter,
or a drive writes per day value for the storage device.
[0151] Example 58 includes the subject matter of any of Examples
54-57, and wherein the means for performing the self-test comprises
means for determining a present number of input/output instructions
per second executable by the storage device.
[0152] Example 59 includes the subject matter of any of Examples
54-58, and wherein the means for performing the self-test comprises
means for determining a present latency of the storage device.
[0153] Example 60 includes the subject matter of any of Examples
54-59, and wherein the means for performing the self-test comprise
means for determining, as a function of a historical trend in drive
writes per day (DWPD), an expected number of drive writes per day
(DWPD) to be performed by the storage device; and means for
determining a change to a present DWPD value caused by an update to
a provisionable space parameter of the storage device.
* * * * *
References