U.S. patent application number 15/720236 was filed with the patent office on 2018-05-31 for technologies for offloading i/o intensive operations to a data storage sled.
The applicant listed for this patent is Intel Corporation. Invention is credited to Francesc Guim Bernat, Karthik Kumar, Mark A. Schmisseur, Thomas Willhalm.
Application Number | 20180150240 15/720236 |
Document ID | / |
Family ID | 62190163 |
Filed Date | 2018-05-31 |
United States Patent
Application |
20180150240 |
Kind Code |
A1 |
Bernat; Francesc Guim ; et
al. |
May 31, 2018 |
TECHNOLOGIES FOR OFFLOADING I/O INTENSIVE OPERATIONS TO A DATA
STORAGE SLED
Abstract
Technologies for offloading I/O intensive workload phases to a
data storage sled include a compute sled. The compute sled is to
execute a workload that includes multiple phases. Each phase is
indicative of a different resource utilization over a time period.
Additionally, the compute sled is to identify an I/O intensive
phase of the workload, wherein the amount of data to be
communicated through a network path between the compute sled and
the data storage sled to execute the I/O intensive phase satisfies
a predefined threshold. The compute sled is also to migrate the
workload to the data storage sled to execute the I/O intensive
phase locally on the data storage sled. Other embodiments as also
described and claimed.
Inventors: |
Bernat; Francesc Guim;
(Barcelona, ES) ; Kumar; Karthik; (Chandler,
AZ) ; Schmisseur; Mark A.; (Phoenix, AZ) ;
Willhalm; Thomas; (Sandhausen, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
62190163 |
Appl. No.: |
15/720236 |
Filed: |
September 29, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62427268 |
Nov 29, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 12/0692 20130101;
H04L 63/1425 20130101; H04L 67/10 20130101; G06F 8/654 20180201;
G06F 13/1652 20130101; G06T 1/60 20130101; G06F 11/3055 20130101;
G06F 2212/402 20130101; G06T 1/20 20130101; H03M 7/42 20130101;
H03M 7/6011 20130101; H04L 41/12 20130101; H04L 41/142 20130101;
H04L 47/2441 20130101; G06F 3/065 20130101; G06F 12/0284 20130101;
G06F 9/5038 20130101; H04L 43/08 20130101; G06F 9/544 20130101;
H01R 13/453 20130101; G06F 11/3034 20130101; G06F 7/06 20130101;
G06F 11/3006 20130101; G06F 21/57 20130101; H04L 12/2881 20130101;
H04L 43/04 20130101; H04L 41/0896 20130101; H04L 67/1014 20130101;
G06F 3/067 20130101; G06F 3/0613 20130101; H01R 13/4538 20130101;
H04L 41/046 20130101; H04L 61/2007 20130101; G06F 9/3851 20130101;
G06F 8/658 20180201; H04L 41/044 20130101; G06F 11/1453 20130101;
H04L 9/0822 20130101; G06F 16/1744 20190101; H05K 7/1452 20130101;
G06F 11/079 20130101; H03M 7/40 20130101; H04L 41/0853 20130101;
G06F 9/5005 20130101; H04L 12/4633 20130101; H03K 19/1731 20130101;
G06F 9/505 20130101; H04L 67/327 20130101; G06F 3/0604 20130101;
G06F 3/0653 20130101; H03M 7/6029 20130101; H05K 7/1487 20130101;
H03M 7/6017 20130101; H04L 47/78 20130101; H04L 67/36 20130101;
H03M 7/3084 20130101; H04L 47/20 20130101; H04L 49/104 20130101;
G06F 3/0617 20130101; G06F 9/4401 20130101; G06F 11/3079 20130101;
G06F 2221/2107 20130101; H04L 43/06 20130101; G06F 3/0608 20130101;
G06F 3/0641 20130101; G06F 8/65 20130101; G06F 21/73 20130101; G06F
21/76 20130101; G06F 9/3891 20130101; G06F 11/0751 20130101; H03M
7/60 20130101; H04L 43/0894 20130101; G06F 11/3409 20130101; G06F
15/80 20130101; G06T 9/005 20130101; H04L 41/0816 20130101; G06F
3/0647 20130101; G06F 8/656 20180201; G06F 9/4881 20130101; G06F
12/023 20130101; G06F 21/6218 20130101; H01R 13/4536 20130101; H01R
13/631 20130101; G06F 11/0709 20130101; G06F 2212/401 20130101;
G06F 3/0611 20130101 |
International
Class: |
G06F 3/06 20060101
G06F003/06 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 30, 2017 |
IN |
201741030632 |
Claims
1. A compute sled comprising: a compute engine to: execute a
workload that includes multiple phases, wherein each phase is
indicative of a different resource utilization over a time period;
identify an I/O intensive phase of the workload wherein an amount
of data to be communicated through a network path between the
compute sled and the data storage sled to execute the I/O intensive
phase satisfies a predefined threshold; and migrate the workload to
the data storage sled to execute the I/O intensive phase locally on
the data storage sled.
2. The compute sled of claim 1, wherein the compute engine is
further to: send memory map data to the data storage sled, wherein
the memory map data is usable by the data storage sled to access
main memory of the compute sled as local memory as the I/O
intensive phase is executed on the data storage sled.
3. The compute sled of claim 1, wherein the compute engine is
further to determine whether the I/O intensive phase will occur
within a predefined time period; and wherein to migrate comprises
to migrate, in response to a determination that the I/O intensive
phase will occur within the predefined time period, the workload to
the data storage sled.
4. The compute sled of claim 3, wherein the compute engine is
further to identify a pattern of phases over time as the workload
is executed; and wherein to determine whether the I/O intensive
phase will occur within a predefined time period comprises to:
determine a likelihood, as a function of a present time and the
identified pattern of phases, that the I/O intensive phase will
occur within the predefined time period; determine whether the
likelihood satisfies a predefined threshold likelihood; and
determine, in response to a determination that the likelihood
satisfies the predefined threshold likelihood, that the I/O
intensive phase will occur within the predefined time period.
5. The compute sled of claim 3, wherein the compute engine is
further to: determine whether the network path between the compute
sled and the data storage sled satisfies a predefined level of
congestion; and wherein to migrate further comprises to migrate, in
response to a determination that the network path satisfies the
predefined level of congestion, the workload to the data storage
sled.
6. The compute sled of claim 5, wherein to determine whether the
network path between the compute sled and the data storage sled
satisfies a predefined level of congestion comprises to determine
whether access of data on the data storage sled through the network
path would reduce the execution speed of the I/O intensive
phase.
7. The compute sled of claim 1, wherein to identify the I/O
intensive phase comprises to identify the I/O intensive phase as a
function of workload metadata that identifies executable code
associated with the I/O intensive phase.
8. The compute sled of claim 1, wherein to identify the I/O
intensive phase comprises to identify the I/O intensive phase with
pattern recognition.
9. The compute sled of claim 8, wherein to identify the I/O
intensive phase with pattern recognition comprises to determine
historical I/O usage associated with different periods of execution
of the workload.
10. The compute sled of claim 1, wherein to migrate the workload to
the data storage sled comprises to send a request to the data
storage sled to execute the I/O intensive phase of the
workload.
11. The compute sled of claim 10, wherein to send the request
comprises to send executable code associated with the I/O intensive
phase to the data storage sled.
12. The compute sled of claim 11, wherein to send the request
comprises to send input data from a main memory of the compute sled
to the data storage sled for use in execution of the I/O intensive
phase.
13. One or more machine-readable storage media comprising a
plurality of instructions stored thereon that, when executed by a
compute sled cause the compute sled to: execute a workload that
includes multiple phases, wherein each phase is indicative of a
different resource utilization over a time period; identify an I/O
intensive phase of the workload wherein an amount of data to be
communicated through a network path between the compute sled and
the data storage sled to execute the I/O intensive phase satisfies
a predefined threshold; and migrate the workload to the data
storage sled to execute the I/O intensive phase locally on the data
storage sled.
14. The one or more machine-readable storage media of claim 13,
wherein the plurality of instructions, when executed, further cause
the compute sled to: send memory map data to the data storage sled,
wherein the memory map data is usable by the data storage sled to
access main memory of the compute sled as local memory as the I/O
intensive phase is executed on the data storage sled.
15. The one or more machine-readable storage media of claim 13,
wherein the plurality of instructions, when executed, further cause
the compute sled to determine whether the I/O intensive phase will
occur within a predefined time period; and wherein to migrate
comprises to migrate, in response to a determination that the I/O
intensive phase will occur within the predefined time period, the
workload to the data storage sled.
16. The one or more machine-readable storage media of claim 15,
wherein the plurality of instructions, when executed, further cause
the compute sled to identify a pattern of phases over time as the
workload is executed; and wherein to determine whether the I/O
intensive phase will occur within a predefined time period
comprises to: determine a likelihood, as a function of a present
time and the identified pattern of phases, that the I/O intensive
phase will occur within the predefined time period; determine
whether the likelihood satisfies a predefined threshold likelihood;
and determine, in response to a determination that the likelihood
satisfies the predefined threshold likelihood, that the I/O
intensive phase will occur within the predefined time period.
17. The one or more machine-readable storage media of claim 15,
wherein the plurality of instructions, when executed, further cause
the compute sled to: determine whether the network path between the
compute sled and the data storage sled satisfies a predefined level
of congestion; and wherein to migrate further comprises to migrate,
in response to a determination that the network path satisfies the
predefined level of congestion, the workload to the data storage
sled.
18. The one or more machine-readable storage media of claim 17,
wherein to determine whether the network path between the compute
sled and the data storage sled satisfies a predefined level of
congestion comprises to determine whether access of data on the
data storage sled through the network path would reduce the
execution speed of the I/O intensive phase.
19. A method comprising: executing, by a compute sled, a workload
that includes multiple phases, wherein each phase is indicative of
a different resource utilization over a time period; identifying,
by the compute sled, an I/O intensive phase of the workload wherein
an amount of data to be communicated through a network path between
the compute sled and the data storage sled to execute the I/O
intensive phase satisfies a predefined threshold; and migrating, by
the compute sled, the workload to the data storage sled to execute
the I/O intensive phase locally on the data storage sled.
20. The method of claim 19, further comprising: sending, by the
compute sled, memory map data to the data storage sled, wherein the
memory map data is usable by the data storage sled to access main
memory of the compute sled as local memory as the I/O intensive
phase is executed on the data storage sled.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims the benefit of U.S.
Provisional Patent Application No. 62/427,268, filed Nov. 29, 2016
and Indian Provisional Patent Application No. 201741030632, filed
Aug. 30, 2017.
BACKGROUND
[0002] Typically, in systems in which data is accessed by a compute
device from remote data storage (e.g., data stored at a location
remote from the compute device within a data center), the network
can become congested when the amount of data requested is
relatively large. As such, other compute devices may be unable to
perform operations that also require the communication of
relatively large amounts of data through the network in a timely
manner (e.g., in accordance with a latency or throughput target
specified in a service level agreement with a customer). In other
words, the network may become a bottleneck for the execution of
workloads in the data center and the compute resources (e.g.,
processors) of the compute devices may be wasted as those resources
sit idle waiting for requested data to arrive. To remedy such
situations, an operator of the data center may spend monetary
resources to install a higher throughput network. However, in many
instances, the capacity of the higher throughput network may go
largely unused, as the times when multiple workloads are
concurrently in I/O intensive phases (e.g., periods of high network
utilization to access remote data storage) may occur only a small
percentage of the total time that the data center is in use.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] 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.
[0004] 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;
[0005] FIG. 2 is a diagram of an example embodiment of a logical
configuration of a rack of the data center of FIG. 1;
[0006] 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;
[0007] 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;
[0008] 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;
[0009] 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;
[0010] FIG. 7 is a diagram of an example embodiment of a sled that
may be used with the rack architecture of FIG. 6;
[0011] FIG. 8 is a diagram of an example embodiment of a rack
architecture to provide support for sleds featuring expansion
capabilities;
[0012] FIG. 9 is a diagram of an example embodiment of a rack
implemented according to the rack architecture of FIG. 8;
[0013] FIG. 10 is a diagram of an example embodiment of a sled
designed for use in conjunction with the rack of FIG. 9;
[0014] 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;
[0015] FIG. 12 is a simplified block diagram of at least one
embodiment of a system for migrating an I/O intensive phase of a
workload from a compute sled to a data storage sled;
[0016] FIG. 13 is a simplified block diagram of at least one
embodiment of a compute sled of the system of FIG. 12;
[0017] FIG. 14 is a simplified block diagram of at least one
embodiment of a data storage sled of the system of FIG. 13;
[0018] FIG. 15 is a simplified block diagram of at least one
embodiment of an environment that may be established by the compute
sled of FIGS. 12 and 13;
[0019] FIG. 16 is a simplified block diagram of at least one
embodiment of an environment that may be established by the data
storage sled of FIGS. 12 and 14;
[0020] FIGS. 17-18 are a simplified flow diagram of at least one
embodiment of a method for migrating an I/O intensive phase of a
workload to a data storage sled that may be performed by the
compute sled of FIGS. 12 and 13;
[0021] FIGS. 19-20 are a simplified flow diagram of at least one
embodiment of a method for accelerating an I/O intensive phase of a
workload from a compute sled that may be performed by the data
storage sled of FIGS. 12 and 14; and
[0022] FIG. 21 is a simplified diagram of phases of execution of a
workload, the sleds on which the workload is executed in each
phase, and the amount of data operated on in each phase.
DETAILED DESCRIPTION OF THE DRAWINGS
[0023] 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.
[0024] 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).
[0025] 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).
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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 919
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 919 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., 250 W) due to improved cooling (i.e., no thermal shadowing,
more space between devices, more room for larger heat sinks,
etc.).
[0040] 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 921 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.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] 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., 250 W), 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.
[0045] 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.
[0046] 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.
[0047] 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 250 W 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.
[0048] 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.
[0049] 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.
[0050] Referring now to FIG. 12, a system 1210 for migrating an I/O
intensive phase of a workload from a compute sled to a data storage
sled 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 1210 includes an
orchestrator server 1216 in communication with a network switch
1220. The network switch 1220 is communicatively coupled to
multiple sleds including compute sleds 1230, 1232, and a data
storage sled 1240. One or more of the sleds 1230, 1232, 1240, may
be grouped into a managed node, such as by the orchestrator server
1216, to collectively perform a workload, such as an application. 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 1216
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 1210 may be
located in a data center and provide storage and compute services
(e.g., cloud services) to a client device 1214 that is in
communication with the system 1210 through a network 1212. The
orchestrator server 1216 may support a cloud operating environment,
such as OpenStack, and managed nodes established by the
orchestrator server 1216 may execute one or more applications or
processes (i.e., workloads), such as in virtual machines or
containers, on behalf of a user of the client device 1214. In the
illustrative embodiment, the compute sled 1230 executes a workload
1234 (e.g., an application), and the compute sled 1232 executes
another workload 1236 (e.g., another application). Further, the
data storage sled 1240 includes multiple data storage devices 1244,
1246 (e.g., physical storage resources 205-1). While two compute
sleds 1230, 1232 and a data storage sled 1240 are shown, it should
be understood that other sleds, such as memory sleds and
accelerator sleds may be present in the system 1210 and may be
selectively added to or removed from a managed node (e.g., as
determined by the orchestrator server 1216).
[0051] In operation, the system 1210 may utilize one or more
migration logic units 1250 in a compute sled 1230, 1232 and/or an
I/O accelerator unit 1260 in a data storage sled 1240 to perform
migration of a workload from a compute sled 1230, 1232 to the data
storage sled 1240 when the workload enters an I/O intensive phase,
indicative a period of execution of the workload in which the
amount of data to be sent through the network between the compute
sled 1230 and the data storage sled 1240 satisfies a predefined
threshold amount (e.g., 8 GB/s per second) and the congestion level
of the network path between the compute sled 1230 and the data
storage sled 1240 satisfies a predefined level of congestion (e.g.,
a predefined latency, a predefined utilization of the total
throughput of the network). In the illustrative embodiment, the
predefined level of congestion is a level of congestion in which,
if the I/O intensive phase of the workload was executed on the
compute sled 1230 and the data used by the I/O intensive phase was
sent through the network 1212 between the compute sled 1230 and the
data storage sled 1240, the speed of execution of the workload
would be slowed. As a result, the workload may not produce a result
in a time period specified in a service level agreement (SLA) with
a customer. By migrating the workload to the data storage sled 1240
for execution, the I/O intensive phase may be executed faster, as
the data utilized by the I/O intensive phase is local to the sled
where the workload is executed. In the illustrative embodiment, the
data storage sled 1240 may map a memory range of the main memory of
the compute sled 1230 to the data storage sled 1240, such that data
(e.g., a relatively small set of output data, compared to a
relatively large amount of input data read from a data storage
device local to the data storage sled 1240) may be read from and
written to the main memory of the compute sled 1230 during
execution of the I/O intensive phase on the data storage sled
1240.
[0052] Referring now to FIG. 13, the compute sled 1230 may be
embodied as any type of compute device capable of performing the
functions described herein, including executing a workload (e.g.,
the workload 1234), determining whether the workload is likely to
enter an I/O intensive phase in a predefined time period (e.g., as
the next phase of the workload, within 10 milliseconds, etc.),
determining whether the path through the network 1212 between the
compute sled and the data sled 1240 is congested enough that the
network 1212 would be a bottleneck in the execution speed of the
workload (e.g., the network 1212 would be unable to transfer data
from the data storage sled 1240 to the compute sled 1230 fast
enough to avoid the compute sled 1230 consuming idle cycles that
could otherwise be spent on executing the workload), migrating the
workload to the data storage sled 1240 for execution of the I/O
intensive phase, and resuming execution of the workload after the
I/O intensive phase has completed.
[0053] As shown in FIG. 13, the illustrative compute sled 1230
includes a compute engine 1302, an input/output (I/O) subsystem
1308, communication circuitry 1310, and one or more data storage
devices 1314. Of course, in other embodiments, the compute sled
1230 may include other or additional components, such as those
commonly found in a computer (e.g., display, peripheral devices,
etc.). Additionally, in some embodiments, one or more of the
illustrative components may be incorporated in, or otherwise form a
portion of, another component.
[0054] 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. Additionally, in some embodiments, the compute engine
1302 includes or is embodied as a processor 1304 and a memory 1306.
The processor 1304 may be embodied as any type of processor capable
of performing the functions described herein. For example, the
processor 1304 may be embodied as a single or multi-core
processor(s), a microcontroller, or other processor or
processing/controlling circuit. In some embodiments, the processor
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. The
processor 1304 may include a migration logic unit 1250 briefly
mentioned with reference to FIG. 12. The migration logic unit 1250
may be embodied as a specialized device, such as a co-processor, an
FPGA, or an ASIC, for determining whether a workload is about to
enter an I/O intensive phase (e.g., as indicated by top-down
microarchitecture analysis method (TMAM) metrics), determining
whether the present network congestion level indicates that the
network would be a bottleneck to the execution of the workload,
migrating the workload to the data storage sled 1240, including
sending memory map data usable for mapping a region of the memory
1306 to the data storage sled 1240 to enable the data storage sled
to read from and/or write to the main memory of the compute sled
1230 (e.g., in a memory region used by the workload 1234) as the
workload is executed by the data storage sled 1240, and
reformatting the data to a format usable by the data storage sled
(e.g., by specialized logic in the data storage sled, such as the
I/O accelerator unit 1260), such as by converting a file to a block
or vice versa, changing a byte ordering of data, etc.
[0055] The main 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.
[0056] 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.
[0057] 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 main memory 1306 may be
integrated into the processor 1304. In operation, the main memory
1306 may store various software and data used during operation such
as workload data, phase data, network congestion data, migration
data, applications, programs, libraries, and drivers.
[0058] The compute engine 1302 is communicatively coupled to other
components of the compute sled 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 processor 1304 and/or the main memory 1306) and other
components of the compute sled 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 processor 1304, the
main memory 1306, and other components of the compute sled 1230,
into the compute engine 1302.
[0059] The communication circuitry 1310 may be embodied as any
communication circuit, device, or collection thereof, capable of
enabling communications over the network 1212 between the compute
sled 1230 and another compute device (e.g., the data storage sled
1240, the orchestrator server 1216, etc.). 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.
[0060] The illustrative communication circuitry 1310 includes a
network interface controller (NIC) 1312, 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 compute sled 1230 to connect with another compute
device (e.g., the data storage sled 1240, the orchestrator server
1216, 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 compute sled 1230 at the board level, socket
level, chip level, and/or other levels. In some embodiments, the
migration logic unit 1250 may be included in the NIC 1312.
[0061] The one or more illustrative data storage devices 1314, 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 data storage device 1314 may
include a system partition that stores data and firmware code for
the data storage device 1314. Each data storage device 1314 may
also include an operating system partition that stores data files
and executables for an operating system.
[0062] Additionally or alternatively, the compute sled 1230 may
include one or more peripheral devices 1316. Such peripheral
devices 1316 may include any type of peripheral device commonly
found in a compute device such as a display, speakers, a mouse, a
keyboard, and/or other input/output devices, interface devices,
and/or other peripheral devices.
[0063] Referring now to FIG. 14, the data storage sled 1240 may be
embodied as any type of compute device capable of performing the
functions described herein, including reading and writing data to
one or more data storage devices of the data storage sled 1240 in
response to corresponding requests from a compute sled 1230,
executing a workload that has been migrated to the data storage
sled 1240 (e.g., an I/O intensive phase of a workload), sending
output data to the compute sled as a result of performing the I/O
intensive phase (e.g., by writing the output data to a region of
the main memory 1306 of the compute sled 1230 that has been mapped
for use by the workload when executed on the data storage sled
1240), and migrating the workload back to the compute sled 1230
after the I/O intensive phase has ended or when the congestion
level of the network has decreased to a point where the network is
no longer a bottleneck for the I/O intensive phase.
[0064] As shown in FIG. 14, the illustrative data storage sled 1240
includes a compute engine 1402, an input/output (I/O) subsystem
1408, communication circuitry 1410, and one or more data storage
devices 1414. Of course, in other embodiments, the data storage
sled 1240 may include other or additional components, such as those
commonly found in a computer (e.g., display, peripheral devices,
etc.). Additionally, in some embodiments, one or more of the
illustrative components may be incorporated in, or otherwise form a
portion of, another component.
[0065] The compute engine 1402 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 1402 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. Additionally, in some embodiments, the compute engine
1402 includes or is embodied as a processor 1404 and a memory 1406.
The processor 1404 may be embodied as any type of processor capable
of performing the functions described herein. For example, the
processor 1404 may be embodied as a single or multi-core
processor(s), a microcontroller, or other processor or
processing/controlling circuit. In some embodiments, the processor
1404 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. The
processor 1404 may include an I/O accelerator unit 1260, which may
be embodied as a specialized device, such as a co-processor, an
FPGA, or an ASIC, for executing the I/O intensive phase of one or
more workloads, using data in the local data storage device(s)
1414. In some embodiments, the I/O accelerator unit 1260 may map a
memory region of the compute sled 1230 as local memory for the
corresponding workload. As such, during execution of the workload,
the I/O accelerator unit 1260 may cause data to be read from and/or
written to the main memory 1306 of the compute sled 1406 (e.g., by
interfacing with the migration logic unit 1250 of the compute sled
1230) as if the memory 1306 was local and without modifying the
executable code of the workload. Additionally, the I/O accelerator
unit 1260 may reformat data from the compute sled 1230 to a format
usable by the I/O accelerator unit 1260 (e.g., converting a file to
a block or vice versa, changing a byte ordering of data, etc.) to
execute the I/O intensive phase of the workload.
[0066] The main memory 1406 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. In operation, the main memory 1406 may store
various software and data used during operation such as workload
data, phase data, network congestion data, migration data,
applications, programs, libraries, and drivers.
[0067] The compute engine 1402 is communicatively coupled to other
components of the data storage sled 1240 via the I/O subsystem
1408, which may be embodied as circuitry and/or components to
facilitate input/output operations with the compute engine 1402
(e.g., with the processor 1404 and/or the main memory 1406) and
other components of the data storage sled 1240. For example, the
I/O subsystem 1408 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
1408 may form a portion of a system-on-a-chip (SoC) and be
incorporated, along with one or more of the processor 1404, the
main memory 1406, and other components of the data storage sled
1240, into the compute engine 1402.
[0068] The communication circuitry 1410 may be embodied as any
communication circuit, device, or collection thereof, capable of
enabling communications over the network 1212 between the data
storage sled 1240 and another compute device (e.g., the compute
sleds 1230, 1232, the orchestrator server 1216, etc.). 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.
[0069] The illustrative communication circuitry 1410 includes a
network interface controller (NIC) 1412, which may also be referred
to as a host fabric interface (HFI). The NIC 1412 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 data storage sled 1240 to connect with another compute
device (e.g., the compute sleds 1240, 1242 the orchestrator server
1216, etc.). In some embodiments, the NIC 1412 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 1412 may
include a local processor (not shown) and/or a local memory (not
shown) that are both local to the NIC 1412. In such embodiments,
the local processor of the NIC 1412 may be capable of performing
one or more of the functions of the compute engine 1402 described
herein. Additionally or alternatively, in such embodiments, the
local memory of the NIC 1412 may be integrated into one or more
components of the compute sled 1240 at the board level, socket
level, chip level, and/or other levels. In some embodiments, the
I/O accelerator unit 1260 may be included in the NIC 1412.
[0070] The one or more illustrative data storage devices 1414, 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 data storage device 1414 may
include a system partition that stores data and firmware code for
the data storage device 1414. Each data storage device 1414 may
also include an operating system partition that stores data files
and executables for an operating system.
[0071] Additionally or alternatively, the data storage sled 1240
may include one or more peripheral devices 1416. Such peripheral
devices 1416 may include any type of peripheral device commonly
found in a compute device such as a display, speakers, a mouse, a
keyboard, and/or other input/output devices, interface devices,
and/or other peripheral devices.
[0072] The client device 1214, the orchestrator server 1216, and
the compute sled 1232 may have components similar to those
described in FIGS. 13 and 14. The description of those components
of the compute sled 1230 and the data storage sled 1240 is equally
applicable to the description of components of those devices and is
not repeated herein for clarity of the description. Further, it
should be appreciated that any of the client device 1214, the
orchestrator server 1216, and the sleds 1230, 1232, 1240 may
include other components, sub-components, and devices commonly
found in a computing device, which are not discussed above in
reference to the compute sled 1230 and the data storage sled 1240
and not discussed herein for clarity of the description.
[0073] As described above, the network switch 1220, the
orchestrator server 1216, and the sleds 1230, 1232, 1240 are
illustratively in communication via the network 1212, 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.
[0074] Referring now to FIG. 15, the compute sled 1230 may
establish an environment 1500 during operation. The illustrative
environment 1500 includes a network communicator 1520 and a
migration manager 1530. 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, migration manager circuitry 1530, etc.). It should
be appreciated that, in such embodiments, one or more of the
network communicator circuitry 1520 or migration manager circuitry
1530 may form a portion of one or more of the compute engine 1302,
the migration logic unit 1250, the communication circuitry 1310,
the I/O subsystem 1308, and/or other components of the compute sled
1230. In the illustrative embodiment, the environment 1500 includes
workload data 1502, which may be embodied as any data indicative of
workloads assigned to the compute sled 1230 to execute, including
an identifier of each workload and executable code associated with
each workload, and a memory region (e.g., a set of memory
addresses) used by each workload to access (e.g., read and/or
write) data in the main memory 1306, and an identifier of a data
storage sled (e.g., the data storage sled 1240) used by each
workload for accessing data storage. Additionally, the illustrative
environment 1500 includes phase data 1504 which may be embodied as
any data indicative of resource utilization characteristics of each
phase of each workload (e.g., phase A exhibits relatively high
processor utilization and low data storage utilization, phase B
exhibits relatively low processor utilization and high data storage
utilization, etc.), detected patterns of phases (e.g., phase A is
typically followed by phase B, then phase A, then phase C, etc.),
and/or metadata indicative of locations in the executable code of
the workloads that mark the beginning and end of each phase. In
addition, the environment 1500 includes network congestion data
1506, which may be embodied as any data indicative of the present
data transfer capacity of the network 1212 (e.g., a latency, a
bandwidth, a throughput, a fullness of a transmit buffer of the
compute sled 1230, etc.). Additionally, the illustrative
environment 1500 includes migration data 1508, which may be
embodied as any data indicative of a determination of whether to
migrate a workload to the data storage sled 1240 (e.g., as a
function of whether an I/O intensive phase is predicted to occur
within a predefined time window and whether the network 1212 is
sufficiently congested to be a bottleneck to the execution of the
I/O intensive phase on the compute sled 1230).
[0075] In the illustrative environment 1500, the network
communicator 1520, 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 compute sled
1230, respectively. To do so, the network communicator 1520 is
configured to receive and process data packets from one system or
computing device (e.g., the orchestrator server 1216) and to
prepare and send data packets to another computing device or system
(e.g., the data storage sled 1240). Accordingly, in some
embodiments, at least a portion of the functionality of the network
communicator 1520 may be performed by the communication circuitry
1310, and, in the illustrative embodiment, by the NIC 1312.
[0076] The migration manager 1530, which may be embodied as
hardware, firmware, software, virtualized hardware, emulated
architecture, and/or a combination thereof, is configured to manage
the migration of a workload to the data storage sled 1240 for
execution if the workload is entering an I/O intensive phase and
the network 1212 would be a bottleneck (e.g., transferring the data
though the network between the data storage sled 1240 and the
compute sled 1230 would slow the execution of the workload). To do
so, in the illustrative embodiment, the migration manager 1530
includes a workload executor 1532, an I/O intensity determiner
1534, a network congestion determiner 1536, and a workload phase
migrator 1538. The workload executor 1532, in the illustrative
embodiment, is configured to execute the workload using data stored
in the data storage device(s) 1414 of the data storage sled 1240.
As the compute sled 1230 executes the workload, the workload may
transition through multiple phases of resource utilization, as
described above. The I/O intensity determiner 1534, in the
illustrative embodiment, is configured to determine whether the
amount of data to be accessed from the data storage device(s) 1414
of the data storage sled 1240 to execute a phase satisfies a
threshold amount (e.g., a predefined number of gigabytes per
second, etc.). In the illustrative embodiment, the I/O intensity
determiner 1534 may monitor the resource utilization of the
workload over time to identify the different phases, identify
patterns in the phases, and/or metadata associated with sections of
the executable code of the workload that demarcate different
phases, to predict whether the workload will transition into an I/O
intensive phase within a predefined time period. The network
congestion determiner 1536, in the illustrative embodiment, is
configured to determine the level of network congestion, such as by
sending a test message to the data storage sled 1240 to determine a
latency in receiving a response from the data storage sled 1240,
identifying a fullness of a transmit buffer of the NIC 1312 of the
compute sled 1230 (e.g., a fuller buffer may indicate more
congestion), and/or by querying the orchestrator server 1216 for
the network congestion data 1506. The workload phase migrator 1538,
in the illustrative embodiment, is configured to determine whether
to migrate the workload to the data storage sled 1240 as a function
of whether the workload is predicted to enter an I/O intensive
phase within a predefined time period (e.g., 10 milliseconds) and
further as a function of the network congestion data 1506 (e.g.,
whether the network 1212 is congested to the point that the network
1212 would be a bottleneck to the execution of the workload in the
I/O intensive phase). Further, in the illustrative embodiment, the
workload phase migrator 1538 may facilitate migration of the
workload to the data storage sled 1240 by providing memory map data
that is usable by the data storage sled 1240 to map a region of the
main memory 1306 of the compute sled 1230 as local memory to be
used by the workload when the workload is executed on the data
storage sled 1240. Additionally, the workload phase migrator 1538
may reformat data in the main memory 1306 to a different format
that is usable by the data storage sled 1240 (e.g., by the I/O
accelerator unit 1260 of the data storage sled 1240), as described
herein.
[0077] It should be appreciated that each of the workload executor
1532, the I/O intensity determiner 1534, the network congestion
determiner 1536, and the workload phase migrator 1538 may be
separately embodied as hardware, firmware, software, virtualized
hardware, emulated architecture, and/or a combination thereof. For
example, the workload executor 1532 may be embodied as a hardware
component, while the I/O intensity determiner 1534, the network
congestion determiner 1536, and the workload phase migrator 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.
[0078] Referring now to FIG. 16, the data storage sled 1240 may
establish an environment 1600 during operation. The illustrative
environment 1600 includes a network communicator 1620 and a
migration manager 1630. Each of the components of the environment
1600 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 1600 may be embodied as circuitry
or a collection of electrical devices (e.g., network communicator
circuitry 1620, migration manager circuitry 1630, etc.). It should
be appreciated that, in such embodiments, one or more of the
network communicator circuitry 1620 or migration manager circuitry
1630 may form a portion of one or more of the compute engine 1402,
the I/O accelerator unit 1260, the communication circuitry 1410,
the I/O subsystem 1408, and/or other components of the data storage
sled 1240. In the illustrative embodiment, the environment 1600
includes workload data 1602, which may be embodied as any data
indicative of workloads assigned to the data storage sled 1240 to
be executed (e.g., in the I/O intensive phase), memory map data
indicative of a memory region of the corresponding compute sled
1230 that may be mapped as local memory as the workload is executed
on the data storage sled 1240, and executable code to execute
(e.g., executable code defining the I/O intensive phase of the
workload). Additionally, the illustrative environment 1600 includes
phase data 1604 which, in the illustrative embodiment, is similar
to the phase data 1504 described above. In addition, the
environment 1600 includes network congestion data 1606 which, in
the illustrative embodiment, is similar to the network congestion
data 1506, described above. Additionally, the illustrative
environment 1600 includes migration data 1608, which is similar to
the migration data 1508 described above with reference to FIG. 15,
except the migration data 1608 is indicative of whether the
workload should be migrated back to the corresponding compute sled
1230 (e.g., the network 1212 is not a bottleneck and/or the I/O
intensive phase has ended).
[0079] In the illustrative environment 1600, the network
communicator 1620, 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 data storage
sled 1240, respectively. To do so, the network communicator 1620 is
configured to receive and process data packets from one system or
computing device (e.g., the orchestrator server 1216) and to
prepare and send data packets to another computing device or system
(e.g., the compute sled 1230). Accordingly, in some embodiments, at
least a portion of the functionality of the network communicator
1620 may be performed by the communication circuitry 1410, and, in
the illustrative embodiment, by the NIC 1412.
[0080] The migration manager 1630, which may be embodied as
hardware, firmware, software, virtualized hardware, emulated
architecture, and/or a combination thereof, is configured to
facilitate the migration of a workload to the data storage sled
1240 and for migrating a workload back to the corresponding compute
sled 1230, 1232 after the I/O intensive phase of the workload has
completed or if the network congestion satisfies a predefined
threshold (e.g., the network would no longer be a bottleneck to the
execution of the I/O intensive phase of the workload). To do so, in
the illustrative embodiment, the migration manager 1630 includes a
phase accelerator 1632, a quality of service (QoS) manager 1634, a
network congestion determiner 1636, and a workload phase migrator
1638. The phase accelerator 1632, in the illustrative embodiment,
is configured to execute a workload that is in an I/O intensive
phase (e.g., with the I/O accelerator unit 1260). The QoS manager
1634, in the illustrative embodiment, is configured to apply a
quality of service (QoS) policy to throttle the usage of resources
by the workloads executed on the data storage sled 1240 so that no
workload dominates the usage of data storage sled resources to the
detriment of other workloads (e.g., causing a workload to no longer
satisfy a QoS target specified in a service level agreement (SLA)).
The network congestion determiner 1636 is similar to the network
congestion determiner 1536 described with reference to the
environment 1500. Additionally, in the illustrative embodiment, the
workload phase migrator 1638 is configured to facilitate the
migration of the workload to the data storage sled 1240, such as by
establishing a memory map that enables the workload to access the
main memory 1306 of the compute sled 1230 as local memory. The
workload phase migrator 1638 is also configured to determine when
to migrate the workload back to the compute sled 1230, 1232. In the
illustrative embodiment, the workload phase migrator 1638 is
configured to migrate the workload back to the compute sled 1230,
1232 when the I/O intensive phase has been completed (e.g., the
executable code of the I/O intensive phase has been completely
executed, the amount of memory bandwidth utilized by the workload
has fallen below a predefined amount, etc.) and/or when the network
congestion has decreased to a level that the network 1212 would no
longer be a bottleneck to the execution of the workload on the
compute sled 1230, 1232.
[0081] It should be appreciated that each of the phase accelerator
1632, the QoS manager 1634, the network congestion determiner 1636,
and the workload phase migrator 1638 may be separately embodied as
hardware, firmware, software, virtualized hardware, emulated
architecture, and/or a combination thereof. For example, the phase
accelerator 1632 may be embodied as a hardware component, while the
QoS manager 1634, the network congestion determiner 1636, and the
workload phase migrator 1638 are embodied as virtualized hardware
components or as some other combination of hardware, firmware,
software, virtualized hardware, emulated architecture, and/or a
combination thereof.
[0082] Referring now to FIG. 17, the compute sled 1230, in
operation, may execute a method 1700 to enable offloading of I/O
intensive phases of a workload to the data storage sled 1240. The
method 1700 begins with block 1702 in which the compute sled 1230
determines whether to execute a workload. The compute sled 1230 may
determine to enable offloading if the compute sled 1230 has been
assigned to a managed node, is powered on and communicatively
coupled to the data storage sled 1240, and/or based on other
factors. Regardless, in response to a determination to execute a
workload, the method 1700 advances to block 1704 in which the
compute sled 1230 receives a workload to be executed (e.g., the
compute sled 1230 receives the workload data 1502 identifying the
workload and the executable code of the workload). In doing so, the
compute sled 1230 may receive the workload from the orchestrator
server 1216, as indicated in block 1706. In later iterations of the
block 1704, the compute sled 1230 may receive a workload from the
data storage sled 1240 (e.g., after the workload has been migrated
to the data storage sled 1240 and the data storage sled 1240 has
completed the I/O intensive phase), as indicated in block 1708.
[0083] In block 1710, the compute sled 1230 executes the workload,
including accessing (e.g., reading from and/or writing to) the data
storage sled 1240 and a region of the main memory 1306 of the
compute sled 1230. Additionally, in block 1712, the compute sled
1230 identifies I/O intensive phases of the workload. In doing so,
the compute sled 1230 may identify phases in which the amount of
data sent through the network (e.g., between the data storage sled
1240 and the compute sled 1230) satisfies a predefined threshold,
such as a predefined number of gigabytes per second, as indicated
in block 1714. As indicated in block 1716, in identifying the I/O
intensive phases, the compute sled 1230 identifies I/O intensive
phases as a function of workload metadata indicative of the I/O
intensive phases. For example, the metadata may be included with
the executable code of the workload and may identify the sections
of the executable code that mark the beginning and end of each
phase. Further, the metadata may indicate the types and amounts of
resources utilized by each phase. As indicated in block 1718, the
compute sled 1230 identifies I/O intensive phases using pattern
recognition. In doing so, and as indicated in block 1720, the
compute sled 1230 may determine historical I/O usage associated
with different periods of execution of the workload and identify
changes in the I/O usage as changes in the phases of the workload.
Further, as indicated in block 1722, the compute sled 1230 may
identify patterns of phases (e.g., phase A, followed by phase B,
then phase A, then phase C, then phase A, etc.).
[0084] In block 1724, the compute sled 1230 determines whether an
I/O intensive phase is likely to occur within a predefined time
period. In doing so, the compute sled 1230, in the illustrative
embodiment, determines a likelihood of an I/O intensive phase
occurring within the predefined time period as a function of the
identified pattern of phases and the present time, as indicated in
block 1726. For example, if the compute sled 1230 has determined
that phase B is I/O intensive, that phase B typically (e.g., 80% of
the time) follows phase A, and that phase A has been executing for
90% of its typical phase residency (i.e., time period of execution)
of 100 milliseconds, then the compute sled 1230 may determine that
the I/O intensive phase (e.g., phase B) is likely to occur within
the next 10 milliseconds. In block 1728, the compute sled 1230
determines the subsequent course of action as a function of whether
there is an upcoming I/O intensive phase in the workload (e.g.,
whether the likelihood of an I/O intensive phase occurring within
the next 10 milliseconds is greater than a predefined threshold,
such as 50%). If the compute sled 1230 determines that there is not
an upcoming I/O intensive phase, the method 1700 loops back to
block 1702 in which the compute sled 1230 determines whether to
continue to enable offloading of I/O intensive phases. Otherwise,
the method 1700 advances to block 1730 of FIG. 18, in which the
compute sled 1230 determines whether the network path to the data
storage sled 1240 satisfies a predefined level of congestion (e.g.,
50% of total capacity, a predefined latency, bandwidth, or
throughput, etc.). In doing so, and as indicated in block 1732, in
the illustrative embodiment, the compute sled 1230 may locally
determine the congestion of the network path between the present
compute sled 1230 and the data storage sled 1240, such as by
sending a test message to the data storage sled 1240 and
determining the latency to receive a response, determining an
amount of time to transfer a test payload, measuring the throughput
and/or latency for data actually used by the workload as it is
being executed on the compute sled 1230, measuring a fullness of a
transmission buffer of the NIC 1312, and/or based on other factors.
As indicated in block 1734, the compute sled 1230 may additionally
or alternatively query the orchestrator server 1216 for the network
congestion data 1506. As indicated in block 1736, the compute sled
1230, in the illustrative embodiment, determines whether the
congestion of the network 1212 would reduce the execution speed of
the I/O intensive phase (e.g., the available data transmission
capacity of the network 1212 is less than the predefined threshold
amount from block 1714 of FIG. 17). In block 1738, the compute sled
1230 determines the subsequent course of action as a function of
whether the network 1212 has been determined to be congested.
[0085] In response to a determination that the network is not
congested, the method 1700 loops back to block 1702 of FIG. 17, in
which the compute sled 1230 determines whether to continue to
enable offloading of I/O intensive phases. Otherwise, the method
1700 advances to block 1740, in which the compute sled 1230
migrates the workload to the data storage sled 1240. In doing so,
and as indicated in block 1742, the compute sled 1230 may send a
request for the I/O intensive phase to be executed by the data
storage sled 1240. The compute sled 1230 may send the request to
the data storage sled 1240, as indicated in block 1744. In doing
so, the compute sled 1230 may send executable code associated with
the I/O intensive phase of the workload to the data storage sled
1240, as indicated in block 1746. As indicated in block 1748, the
compute sled 1230 may send an identifier of the I/O intensive phase
to the data storage sled 1240. Further, as indicated in block 1750,
the compute sled 1230 may send memory map data usable by the data
storage sled 1240 to access a portion of the main memory 1306 of
the compute sled 1230 as local memory of the data storage sled 1240
(e.g., transparently to the workload). As indicated in block 1752,
the compute sled 1230 may format input data associated with the I/O
intensive phase to a format usable by the I/O accelerator unit 1260
of the data storage sled 1240. For example, the compute sled 1230
may convert a file to a block or vice versa, change a byte ordering
of data, and/or perform other reformatting of data. The compute
sled 1230 may also send input data from the main memory 1306 of the
compute sled 1230 for use in execution of the I/O intensive phase
(e.g., the reformatted data from block 1752), as indicated in block
1754. In general, the amount of input data from the main memory
1306 is smaller (e.g., an order of magnitude smaller) than the
amount of data in the data storage device(s) 1414 to be used by the
workload during the I/O intensive phase. As indicated in block
1756, rather than, or in addition to sending the migration request
to the data storage sled 1240, the compute sled 1230 may send the
request to the orchestrator server 1216 to then be sent to the data
storage sled 1240. Subsequently, the method 1700 loops back to
block 1702 of FIG. 17 in which the compute sled 1230 determines
whether to continue to enable offloading of I/O intensive
phases.
[0086] Referring now to FIG. 19, the data storage sled 1240, in
operation, may execute a method 1900 to accelerate I/O intensive
phases of workloads offloaded from compute sleds 1230, 1232. The
method 1900 begins with block 1902 in which the data storage sled
1240 determines whether to accelerate one or more I/O intensive
phases of workload(s). In the illustrative embodiment, the data
storage sled 1240 may determine to accelerate an I/O intensive
phase if the data storage sled 1240 has received a request to
migrate a workload (e.g., the request of block 1740 described with
reference to FIG. 18). In other embodiments, the data storage sled
1240 may make the determination based on other factors. Regardless,
in response to a determination to accelerate an I/O intensive phase
of a workload, the method 1900 advances to block 1904, in which the
data storage sled 1240 executes the I/O intensive phase. In doing
so, the data storage sled 1240 may access a relatively large amount
of data in a data storage device 1414 of the data storage sled 1240
(e.g., tens of gigabytes), as indicated in block 1906. In addition,
as indicated in block 1908, the data storage sled 1240 may receive
a relatively small set of input data from the compute sled 1230
(e.g., 100 megabytes). As indicated in block 1910, the compute sled
1230 may send a relatively small set of output data to the compute
sled 1230 (e.g., a 10 megabyte result of a computation). In the
illustrative embodiment, and as indicated in block 1912, the data
storage sled 1240 may map one or more local memory addresses to the
main memory 1306 of the compute sled 1230 (e.g., using the memory
map data from block 1750 of FIG. 18). In block 1914, in executing
the I/O intensive phase, the data storage sled 1240 may access the
main memory 1306 of the compute sled 1230. In accessing the main
memory 1306 of the compute sled 1230, the data storage sled 1240
may read input data for the I/O intensive phase from the mapped
memory, as indicated in block 1916. The data storage sled 1240 may
reformat the input data to a format usable by the I/O accelerator
unit (e.g., if the input is unusable in its present form and the
compute sled 1230 did not reformat the data in block 1752 of FIG.
18), as indicated in block 1918. As indicated in block 1920, the
data storage sled 1240 may read executable code of the I/O
intensive phase with the mapped memory. In addition, the data
storage sled 1240 may write output data to the main memory of the
compute sled 1230 (e.g., the output data from block 1910), as
indicated in block 1922. As indicated in block 1924, the data
storage sled 1240 may apply a quality of service (QoS) policy to
throttle the usage of resources by the workloads executed on the
data storage sled 1240 so that no workload dominates the usage of
data storage sled resources to the detriment of other workloads
(e.g., causing a workload to no longer satisfy a QoS target
specified in a service level agreement (SLA)).
[0087] In block 1926, the data storage sled 1240 determines whether
the I/O intensive phase has ended. For example, and as indicated in
block 1928, the data storage sled determines whether executable
code associated with the I/O intensive phase has been completely
executed (e.g., the executable code sent by the compute sled 1230
in block 1746 of FIG. 18 has been completely executed).
Subsequently, in block 1930, the data storage sled 1240 determines
the next course of action as a function of whether the I/O
intensive phase has ended. If not, the method 1900 continues to
block 1932, in which the data storage sled 1240 determines whether
the network path to the compute sled 1230 satisfies a predefined
level of congestion (e.g., the congestion in the network would
cause the workload to execute more slowly if it were performed on
the compute sled 1230). In block 1934, the data storage sled 1240
determines the subsequent course of action as a function of whether
the network satisfies the predefined level of congestion. If so,
the method 1900 loops back to block 1904 to continue executing the
I/O intensive phase on the data storage sled 1240. Otherwise, or if
the data storage sled 1240 determines that the phase ended in block
1930, the method 1900 advances to block 1936 of FIG. 20, in which
the data storage sled 1240 migrates execution of the workload to
the compute sled 1230. In doing so, the data storage sled 1240 may
send a message to the compute sled 1230 that the I/O intensive
phase has ended, as indicated in block 1938 (e.g., thereby
migrating execution of the workload back to the compute sled 1230).
Additionally or alternatively, the data storage sled 1240 may send
a message to the orchestrator server 1216 that the I/O phase has
ended, as indicated in block 1940. Subsequently, the method 1900
loops back to block 1902 of FIG. 19, in which the data storage sled
1240 determines whether to continue accelerating one or more I/O
intensive phases.
[0088] Referring now to FIG. 21, a diagram 2100 illustrates that
over time, a workload 1234 may be executed on the data storage sled
1240 in one phase (e.g., phase 0) in which the amount of data
operated on is relatively large (e.g., 10 gigabytes). In subsequent
phases (e.g., phases 1 and 2) which are not I/O intensive, the
workload is executed on the compute sled 1230 during which time the
data operated on and produced is relatively small (e.g., 100
megabytes in phase 1 and 10 megabytes in phase 2).
EXAMPLES
[0089] 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.
[0090] Example 1 includes a compute sled comprising a compute
engine to execute a workload that includes multiple phases, wherein
each phase is indicative of a different resource utilization over a
time period; identify an I/O intensive phase of the workload
wherein an amount of data to be communicated through a network path
between the compute sled and the data storage sled to execute the
I/O intensive phase satisfies a predefined threshold; and migrate
the workload to the data storage sled to execute the I/O intensive
phase locally on the data storage sled.
[0091] Example 2 includes the subject matter of Example 1, and
wherein the compute engine is further to send memory map data to
the data storage sled, wherein the memory map data is usable by the
data storage sled to access main memory of the compute sled as
local memory as the I/O intensive phase is executed on the data
storage sled.
[0092] Example 3 includes the subject matter of any of Examples 1
and 2, and wherein the compute engine is further to determine
whether the I/O intensive phase will occur within a predefined time
period; and wherein to migrate comprises to migrate, in response to
a determination that the I/O intensive phase will occur within the
predefined time period, the workload to the data storage sled.
[0093] Example 4 includes the subject matter of any of Examples
1-3, and wherein the compute engine is further to identify a
pattern of phases over time as the workload is executed; and
wherein to determine whether the I/O intensive phase will occur
within a predefined time period comprises to determine a
likelihood, as a function of a present time and the identified
pattern of phases, that the I/O intensive phase will occur within
the predefined time period; determine whether the likelihood
satisfies a predefined threshold likelihood; and determine, in
response to a determination that the likelihood satisfies the
predefined threshold likelihood, that the I/O intensive phase will
occur within the predefined time period.
[0094] Example 5 includes the subject matter of any of Examples
1-4, and wherein the compute engine is further to determine whether
the network path between the compute sled and the data storage sled
satisfies a predefined level of congestion; and wherein to migrate
further comprises to migrate, in response to a determination that
the network path satisfies the predefined level of congestion, the
workload to the data storage sled.
[0095] Example 6 includes the subject matter of any of Examples
1-5, and wherein to determine whether the network path between the
compute sled and the data storage sled satisfies a predefined level
of congestion comprises to determine whether access of data on the
data storage sled through the network path would reduce the
execution speed of the I/O intensive phase.
[0096] Example 7 includes the subject matter of any of Examples
1-6, and wherein to identify the I/O intensive phase comprises to
identify the I/O intensive phase as a function of workload metadata
that identifies executable code associated with the I/O intensive
phase.
[0097] Example 8 includes the subject matter of any of Examples
1-7, and wherein to identify the I/O intensive phase comprises to
identify the I/O intensive phase with pattern recognition.
[0098] Example 9 includes the subject matter of any of Examples
1-8, and wherein to identify the I/O intensive phase with pattern
recognition comprises to determine historical I/O usage associated
with different periods of execution of the workload.
[0099] Example 10 includes the subject matter of any of Examples
1-9, and wherein to migrate the workload to the data storage sled
comprises to send a request to the data storage sled to execute the
I/O intensive phase of the workload.
[0100] Example 11 includes the subject matter of any of Examples
1-10, and wherein to send the request comprises to send executable
code associated with the I/O intensive phase to the data storage
sled.
[0101] Example 12 includes the subject matter of any of Examples
1-11, and wherein to send the request comprises to send input data
from a main memory of the compute sled to the data storage sled for
use in execution of the I/O intensive phase.
[0102] Example 13 includes the subject matter of any of Examples
1-12, and wherein the compute sled is further to reformat the input
data to a format usable by an I/O accelerator unit of the data
storage sled.
[0103] Example 14 includes a method comprising executing, by a
compute sled, a workload that includes multiple phases, wherein
each phase is indicative of a different resource utilization over a
time period; identifying, by the compute sled, an I/O intensive
phase of the workload wherein an amount of data to be communicated
through a network path between the compute sled and the data
storage sled to execute the I/O intensive phase satisfies a
predefined threshold; and migrating, by the compute sled, the
workload to the data storage sled to execute the I/O intensive
phase locally on the data storage sled.
[0104] Example 15 includes the subject matter of Example 14, and
further including sending, by the compute sled, memory map data to
the data storage sled, wherein the memory map data is usable by the
data storage sled to access main memory of the compute sled as
local memory as the I/O intensive phase is executed on the data
storage sled.
[0105] Example 16 includes the subject matter of any of Examples 14
and 15, and further including determining, by the compute sled,
whether the I/O intensive phase will occur within a predefined time
period; and wherein migrating comprises migrating, in response to a
determination that the I/O intensive phase will occur within the
predefined time period, the workload to the data storage sled.
[0106] Example 17 includes the subject matter of any of Examples
14-16, and further including identifying, by the compute sled, a
pattern of phases over time as the workload is executed; and
wherein determining whether the I/O intensive phase will occur
within a predefined time period comprises determining a likelihood,
as a function of a present time and the identified pattern of
phases, that the I/O intensive phase will occur within the
predefined time period; determining whether the likelihood
satisfies a predefined threshold likelihood; and determining, in
response to a determination that the likelihood satisfies the
predefined threshold likelihood, that the I/O intensive phase will
occur within the predefined time period.
[0107] Example 18 includes the subject matter of any of Examples
14-17, and further including determining, by the compute sled,
whether the network path between the compute sled and the data
storage sled satisfies a predefined level of congestion; and
wherein migrating further comprises migrating, in response to a
determination that the network path satisfies the predefined level
of congestion, the workload to the data storage sled.
[0108] Example 19 includes the subject matter of any of Examples
14-18, and wherein determining whether the network path between the
compute sled and the data storage sled satisfies a predefined level
of congestion comprises determining whether access of data on the
data storage sled through the network path would reduce the
execution speed of the I/O intensive phase.
[0109] Example 20 includes the subject matter of any of Examples
14-19, and wherein identifying the I/O intensive phase comprises
identifying the I/O intensive phase as a function of workload
metadata that identifies executable code associated with the I/O
intensive phase.
[0110] Example 21 includes the subject matter of any of Examples
14-20, and wherein identifying the I/O intensive phase comprises
identifying the I/O intensive phase with pattern recognition.
[0111] Example 22 includes the subject matter of any of Examples
14-21, and wherein identifying the I/O intensive phase with pattern
recognition comprises determining historical I/O usage associated
with different periods of execution of the workload.
[0112] Example 23 includes the subject matter of any of Examples
14-22, and wherein migrating the workload to the data storage sled
comprises sending a request to the data storage sled to execute the
I/O intensive phase of the workload.
[0113] Example 24 includes the subject matter of any of Examples
14-23, and wherein sending the request comprises sending executable
code associated with the I/O intensive phase to the data storage
sled.
[0114] Example 25 includes the subject matter of any of Examples
14-24, and wherein sending the request comprises sending input data
from main memory of the compute sled to the data storage sled for
use in execution of the I/O intensive phase.
[0115] Example 26 includes the subject matter of any of Examples
14-25, and further including reformatting, by the compute sled, the
input data to a format usable by an I/O accelerator unit of the
data storage sled.
[0116] Example 27 includes one or more machine-readable storage
media comprising a plurality of instructions stored thereon that,
in response to being executed, cause a compute sled to perform the
method of any of Examples 14-26.
[0117] Example 28 includes a network device comprising one or more
processors; one or more memory devices having stored therein a
plurality of instructions that, when executed by the one or more
processors, cause the network device to perform the method of any
of Examples 14-26.
[0118] Example 29 includes a compute sled comprising means for
performing the method of any of Examples 14-26.
[0119] Example 30 includes a compute sled comprising means for
executing a workload that includes multiple phases, wherein each
phase is indicative of a different resource utilization over a time
period; means for identifying an I/O intensive phase of the
workload wherein an amount of data to be communicated through a
network path between the compute sled and the data storage sled to
execute the I/O intensive phase satisfies a predefined threshold;
and means for migrating the workload to the data storage sled to
execute the I/O intensive phase locally on the data storage
sled.
[0120] Example 31 includes the subject matter of Example 30, and
further including means for sending memory map data to the data
storage sled, wherein the memory map data is usable by the data
storage sled to access main memory of the compute sled as local
memory as the I/O intensive phase is executed on the data storage
sled.
[0121] Example 32 includes the subject matter of any of Examples 30
and 31, and further including means for determining whether the I/O
intensive phase will occur within a predefined time period; and
wherein the means for migrating comprises means for migrating, in
response to a determination that the I/O intensive phase will occur
within the predefined time period, the workload to the data storage
sled.
[0122] Example 33 includes the subject matter of any of Examples
30-32, and further including means for identifying a pattern of
phases over time as the workload is executed; and wherein the means
for determining whether the I/O intensive phase will occur within a
predefined time period comprises means for determining a
likelihood, as a function of a present time and the identified
pattern of phases, that the I/O intensive phase will occur within
the predefined time period; means for determining whether the
likelihood satisfies a predefined threshold likelihood; and means
for determining, in response to a determination that the likelihood
satisfies the predefined threshold likelihood, that the I/O
intensive phase will occur within the predefined time period.
[0123] Example 34 includes the subject matter of any of Examples
30-33, and further including means for determining whether the
network path between the compute sled and the data storage sled
satisfies a predefined level of congestion; and wherein the means
for migrating further comprises means for migrating, in response to
a determination that the network path satisfies the predefined
level of congestion, the workload to the data storage sled.
[0124] Example 35 includes the subject matter of any of Examples
30-34, and wherein the means for determining whether the network
path between the compute sled and the data storage sled satisfies a
predefined level of congestion comprises means for determining
whether access of data on the data storage sled through the network
path would reduce the execution speed of the I/O intensive
phase.
[0125] Example 36 includes the subject matter of any of Examples
30-35, and wherein the means for identifying the I/O intensive
phase comprises means for identifying the I/O intensive phase as a
function of workload metadata that identifies executable code
associated with the I/O intensive phase.
[0126] Example 37 includes the subject matter of any of Examples
30-36, and wherein the means for identifying the I/O intensive
phase comprises means for identifying the I/O intensive phase with
pattern recognition.
[0127] Example 38 includes the subject matter of any of Examples
30-37, and wherein the means for identifying the I/O intensive
phase with pattern recognition comprises means for determining
historical I/O usage associated with different periods of execution
of the workload.
[0128] Example 39 includes the subject matter of any of Examples
30-38, and wherein the means for migrating the workload to the data
storage sled comprises means for sending a request to the data
storage sled to execute the I/O intensive phase of the
workload.
[0129] Example 40 includes the subject matter of any of Examples
30-39, and wherein the means for sending the request comprises
means for sending executable code associated with the I/O intensive
phase to the data storage sled.
[0130] Example 41 includes the subject matter of any of Examples
30-40, and wherein the means for sending the request comprises
means for sending input data from main memory of the compute sled
to the data storage sled for use in execution of the I/O intensive
phase.
[0131] Example 42 includes the subject matter of any of Examples
30-41, and further including means for reformatting the input data
to a format usable by an I/O accelerator unit of the data storage
sled.
[0132] Example 43 includes a data storage sled comprising a compute
engine to execute an I/O intensive phase of a workload, wherein the
I/O intensive phase is indicative of a period of execution in which
an amount of data to be accessed from a data storage device of the
data storage sled satisfies a predefined threshold; determine
whether the I/O intensive phase has ended; and migrate, in response
to a determination that the I/O intensive phase has ended, the
execution of the workload to a compute sled.
[0133] Example 44 includes the subject matter of Example 43, and
wherein the compute engine is further to determine whether a
network path to the compute sled satisfies a predefined level of
congestion; and migrate, in response to a determination that the
network path does not satisfy the predefined level of congestion,
execution of the workload to the compute sled.
[0134] Example 45 includes the subject matter of any of Examples 43
and 44, and wherein the compute engine is further to map a memory
region to a main memory of the compute sled; and access data in the
main memory of the compute sled as the I/O intensive phase is
executed on the data storage sled.
[0135] Example 46 includes the subject matter of any of Examples
43-45, and wherein the compute engine is further to receive
executable code associated with the I/O intensive phase from the
compute sled; and wherein to execute the I/O intensive phase
comprises to execute the received executable code.
[0136] Example 47 includes the subject matter of any of Examples
43-46, and wherein the compute engine is further to receive an
input set of data from the compute sled; and reformat the input set
of data to a format that is usable by an I/O accelerator unit of
the data storage sled.
[0137] Example 48 includes the subject matter of any of Examples
43-47, and wherein the compute engine is further to execute
multiple I/O intensive phases of different workloads concurrently;
and apply a quality of service management policy to the execution
of the workloads to maintain a target quality of service as the I/O
intensive phases are executed.
[0138] Example 49 includes the subject matter of any of Examples
43-48, and wherein the compute engine is further to send output
data from execution of the I/O intensive phase to the compute
sled.
[0139] Example 50 includes the subject matter of any of Examples
43-49, and wherein the compute engine is to receive a first set of
input data from the compute sled and access a second set of input
data from a data storage device of the data storage sled, wherein
the first and second sets of input data are usable to execute the
I/O intensive phase and the second data set is larger than the
first data set.
[0140] Example 51 includes the subject matter of any of Examples
43-50, and wherein to determine whether the I/O intensive phase has
ended comprises to determine whether executable code associated
with the I/O intensive phase has been completely executed.
[0141] Example 52 includes the subject matter of any of Examples
43-51, and wherein to execute the I/O intensive phase comprises to
execute the I/O intensive phase with an I/O accelerator unit of the
data storage sled.
[0142] Example 53 includes a method comprising executing, by a data
storage sled, an I/O intensive phase of a workload, wherein the I/O
intensive phase is indicative of a period of execution in which an
amount of data to be accessed from a data storage device of the
data storage sled satisfies a predefined threshold; determining, by
the data storage sled, whether the I/O intensive phase has ended;
and migrating, by the data storage sled and in response to a
determination that the I/O intensive phase has ended, the execution
of the workload to a compute sled.
[0143] Example 54 includes the subject matter of Example 53, and
further including determining, by the data storage sled, whether a
network path to the compute sled satisfies a predefined level of
congestion; and migrating, by the data storage sled and in response
to a determination that the network path does not satisfy the
predefined level of congestion, execution of the workload to the
compute sled.
[0144] Example 55 includes the subject matter of any of Examples 53
and 54, and further including mapping, by the data storage sled, a
memory region to a main memory of the compute sled; and accessing
data in the main memory of the compute sled as the I/O intensive
phase is executed on the data storage sled.
[0145] Example 56 includes the subject matter of any of Examples
53-55, and further including receiving, by the data storage sled,
executable code associated with the I/O intensive phase from the
compute sled; and wherein executing the I/O intensive phase
comprises executing the received executable code.
[0146] Example 57 includes the subject matter of any of Examples
53-56, and further including receiving, by the data storage sled,
an input set of data from the compute sled; and reformatting, by
the data storage sled, the input set of data to a format that is
usable by an I/O accelerator unit of the data storage sled.
[0147] Example 58 includes the subject matter of any of Examples
53-57, and further including executing, by the data storage sled,
multiple I/O intensive phases of different workloads concurrently;
and applying, by the data storage sled, a quality of service
management policy to the execution of the workloads to maintain a
target quality of service as the I/O intensive phases are
executed.
[0148] Example 59 includes the subject matter of any of Examples
53-58, and further including sending, by the data storage sled,
output data from execution of the I/O intensive phase to the
compute sled.
[0149] Example 60 includes the subject matter of any of Examples
53-59, and further including receiving, by the data storage sled, a
first set of input data from the compute sled; and accessing, by
the data storage sled, a second set of input data from a data
storage device of the data storage sled, wherein the first and
second sets of input data are usable to execute the I/O intensive
phase and the second data set is larger than the first data
set.
[0150] Example 61 includes the subject matter of any of Examples
53-60, and wherein determining whether the I/O intensive phase has
ended comprises determining whether executable code associated with
the I/O intensive phase has been completely executed.
[0151] Example 62 includes the subject matter of any of Examples
53-61, and wherein executing the I/O intensive phase comprises
executing the I/O intensive phase with an I/O accelerator unit of
the data storage sled.
[0152] Example 63 includes one or more machine-readable storage
media comprising a plurality of instructions stored thereon that,
in response to being executed, cause a data storage sled to perform
the method of any of Examples 53-62.
[0153] Example 64 includes a data storage sled comprising means for
performing the method of any of Examples 53-62.
[0154] Example 65 includes a data storage sled comprising means for
executing an I/O intensive phase of a workload, wherein the I/O
intensive phase is indicative of a period of execution in which an
amount of data to be accessed from a data storage device of the
data storage sled satisfies a predefined threshold; means for
determining whether the I/O intensive phase has ended; and means
for migrating, in response to a determination that the I/O
intensive phase has ended, the execution of the workload to a
compute sled.
[0155] Example 66 includes the subject matter of Example 65, and
further including means for determining whether a network path to
the compute sled satisfies a predefined level of congestion; and
means for migrating, in response to a determination that the
network path does not satisfy the predefined level of congestion,
execution of the workload to the compute sled.
[0156] Example 67 includes the subject matter of any of Examples 65
and 66, and further including means for mapping a memory region to
a main memory of the compute sled; and means for accessing data in
the main memory of the compute sled as the I/O intensive phase is
executed on the data storage sled.
[0157] Example 68 includes the subject matter of any of Examples
65-67, and further including means for receiving executable code
associated with the I/O intensive phase from the compute sled; and
wherein the means for executing the I/O intensive phase comprises
means for executing the received executable code.
[0158] Example 69 includes the subject matter of any of Examples
65-68, and further including means for receiving an input set of
data from the compute sled; and means for reformatting the input
set of data to a format that is usable by an I/O accelerator unit
of the data storage sled.
[0159] Example 70 includes the subject matter of any of Examples
65-69, and further including means for executing multiple I/O
intensive phases of different workloads concurrently; and means for
applying a quality of service management policy to the execution of
the workloads to maintain a target quality of service as the I/O
intensive phases are executed.
[0160] Example 71 includes the subject matter of any of Examples
65-70, and further including means for sending output data from
execution of the I/O intensive phase to the compute sled.
[0161] Example 72 includes the subject matter of any of Examples
65-71, and further including means for receiving a first set of
input data from the compute sled; and means for accessing a second
set of input data from a data storage device of the data storage
sled, wherein the first and second sets of input data are usable to
execute the I/O intensive phase and the second data set is larger
than the first data set.
[0162] Example 73 includes the subject matter of any of Examples
65-72, and wherein the means for determining whether the I/O
intensive phase has ended comprises means for determining whether
executable code associated with the I/O intensive phase has been
completely executed.
[0163] Example 74 includes the subject matter of any of Examples
65-73, and wherein the means for executing the I/O intensive phase
comprises means for executing the I/O intensive phase with an I/O
accelerator unit of the data storage sled.
* * * * *
References