U.S. patent application number 15/396173 was filed with the patent office on 2018-01-25 for techniques to determine and process metric data for physical resources.
This patent application is currently assigned to INTEL CORPORATION. The applicant listed for this patent is INTEL CORPORATION. Invention is credited to MOHAN J. KUMAR, MURUGASAMY K. NACHIMUTHU.
Application Number | 20180027063 15/396173 |
Document ID | / |
Family ID | 60804962 |
Filed Date | 2018-01-25 |
United States Patent
Application |
20180027063 |
Kind Code |
A1 |
NACHIMUTHU; MURUGASAMY K. ;
et al. |
January 25, 2018 |
TECHNIQUES TO DETERMINE AND PROCESS METRIC DATA FOR PHYSICAL
RESOURCES
Abstract
Various embodiments are generally directed to an apparatus,
method and other techniques for communicating metric data between a
plurality of management controllers for sleds via an out-of-band
(OOB) network, the sleds comprising physical resources and the
metric data to indicate one or more metrics for the physical
resources. Embodiments may also include determining a physical
resource of the physical resources to perform a task based at least
in part on the one or more metrics, and causing the task to be
performed by the physical resources.
Inventors: |
NACHIMUTHU; MURUGASAMY K.;
(BEAVERTON, OR) ; KUMAR; MOHAN J.; (ALOHA,
OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTEL CORPORATION |
SANTA CLARA |
CA |
US |
|
|
Assignee: |
INTEL CORPORATION
SANTA CLARA
CA
|
Family ID: |
60804962 |
Appl. No.: |
15/396173 |
Filed: |
December 30, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62427268 |
Nov 29, 2016 |
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62376859 |
Aug 18, 2016 |
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62365969 |
Jul 22, 2016 |
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Current U.S.
Class: |
709/226 |
Current CPC
Class: |
G06F 3/0613 20130101;
G06F 11/3414 20130101; H04L 49/357 20130101; H04Q 11/0062 20130101;
G06F 9/5044 20130101; G06F 9/5072 20130101; H03M 7/30 20130101;
H04L 67/1014 20130101; G06F 12/1408 20130101; G06F 2212/7207
20130101; G06Q 10/06 20130101; G11C 14/0009 20130101; G06F 3/0665
20130101; G06F 3/067 20130101; G06F 3/0673 20130101; G06F 2212/1008
20130101; G06F 2212/1041 20130101; G11C 11/56 20130101; H05K
7/20745 20130101; Y04S 10/50 20130101; G06F 8/65 20130101; G06F
9/5016 20130101; H04L 41/0813 20130101; H04L 41/145 20130101; G06Q
10/06314 20130101; G11C 7/1072 20130101; G02B 6/3893 20130101; G06F
3/0664 20130101; G06F 13/409 20130101; H04L 9/3263 20130101; H04L
41/046 20130101; H04L 43/0894 20130101; H04L 47/782 20130101; H04L
67/02 20130101; H04L 67/12 20130101; H05K 7/20836 20130101; G06F
13/1668 20130101; G06F 2212/401 20130101; G06Q 10/087 20130101;
G06Q 50/04 20130101; H04B 10/25891 20200501; H04L 41/147 20130101;
H04L 49/45 20130101; H04L 67/1029 20130101; H04Q 1/04 20130101;
G06F 13/1694 20130101; G06F 2209/483 20130101; G06F 2212/402
20130101; H05K 7/1421 20130101; G06F 3/0655 20130101; G06F 9/30036
20130101; G06F 9/5027 20130101; G06F 9/505 20130101; G06F 9/544
20130101; G06F 11/141 20130101; G06F 13/161 20130101; H03M 7/4081
20130101; H04L 9/0643 20130101; H05K 7/1485 20130101; G06F 3/0611
20130101; G06F 3/0616 20130101; G06F 3/0619 20130101; G06F 12/0862
20130101; G06F 2212/1044 20130101; G06F 2212/152 20130101; H03M
7/6023 20130101; H04Q 2213/13523 20130101; G05D 23/1921 20130101;
G06F 3/0647 20130101; H04L 9/3247 20130101; H04L 67/1034 20130101;
H04L 69/04 20130101; G06F 3/0689 20130101; G07C 5/008 20130101;
H04L 47/24 20130101; H04W 4/80 20180201; H05K 7/1489 20130101; H05K
7/1498 20130101; H05K 7/20727 20130101; G02B 6/4452 20130101; H04L
47/38 20130101; H04Q 11/0003 20130101; H04Q 2011/0037 20130101;
H04Q 2011/0079 20130101; H05K 7/1491 20130101; G06F 3/0631
20130101; G06F 9/5077 20130101; G08C 2200/00 20130101; H04L 45/02
20130101; H05K 7/1422 20130101; H05K 2201/066 20130101; Y10S 901/01
20130101; B65G 1/0492 20130101; G06F 9/3887 20130101; G06F 13/385
20130101; G06F 15/161 20130101; H04L 41/5019 20130101; H04L 47/823
20130101; H04Q 1/09 20130101; H04Q 2011/0041 20130101; H05K 13/0486
20130101; G02B 6/3882 20130101; G06F 1/20 20130101; H04L 43/0876
20130101; H04L 49/25 20130101; H05K 1/0203 20130101; H05K 7/20709
20130101; H05K 2201/10189 20130101; G05D 23/2039 20130101; G06F
12/0893 20130101; G06F 12/109 20130101; H04L 47/765 20130101; G02B
6/4292 20130101; G06F 3/0625 20130101; G06F 3/0683 20130101; H04L
43/08 20130101; H04L 49/15 20130101; H04Q 2011/0073 20130101; G06F
12/10 20130101; G11C 5/06 20130101; H03M 7/4056 20130101; H04Q
2011/0086 20130101; H05K 7/20736 20130101; G06F 15/8061 20130101;
G08C 17/02 20130101; H04L 41/024 20130101; H04L 43/065 20130101;
H04L 67/16 20130101; H05K 7/1492 20130101; Y02P 90/30 20151101;
B25J 15/0014 20130101; G06F 3/064 20130101; G06F 3/0658 20130101;
G06F 16/9014 20190101; G06F 2212/202 20130101; H03M 7/4031
20130101; H04L 41/12 20130101; H04L 49/00 20130101; H04L 67/1008
20130101; H04L 69/329 20130101; H04Q 11/00 20130101; G06F 1/183
20130101; G06F 3/061 20130101; H03M 7/40 20130101; H03M 7/6005
20130101; H04L 47/805 20130101; H04L 47/82 20130101; H04L 49/555
20130101; H05K 1/181 20130101; Y02D 10/00 20180101; G06F 3/0638
20130101; G06F 13/4068 20130101; H04L 41/0896 20130101; H04L 45/52
20130101; H04L 67/1004 20130101; H04L 67/306 20130101; H05K
2201/10121 20130101; G06F 2209/5019 20130101; H04L 67/10 20130101;
H04Q 2011/0052 20130101; H05K 7/1442 20130101; H05K 7/1447
20130101; G06F 13/42 20130101; H04L 67/1012 20130101; G06F 3/0679
20130101; G06F 3/0688 20130101; G06F 9/4401 20130101; H04Q
2213/13527 20130101; G06F 3/0659 20130101; G06F 2212/1024 20130101;
H03M 7/3084 20130101; H03M 7/3086 20130101; H04W 4/023 20130101;
H05K 7/2039 20130101; H04L 67/1097 20130101; H05K 7/1418 20130101;
H05K 7/1461 20130101; G02B 6/3897 20130101; G06F 13/4282 20130101;
G06F 2209/5022 20130101; H04L 29/12009 20130101; H04L 43/0817
20130101; H05K 2201/10159 20130101; G06F 3/0653 20130101; H04B
10/25 20130101; H04L 9/14 20130101; H04L 43/16 20130101; H04Q
11/0005 20130101; G06F 3/065 20130101; G06F 9/4881 20130101; H04L
12/2809 20130101; H04L 41/082 20130101; H04L 49/35 20130101; G06F
13/4022 20130101; G06Q 10/20 20130101; G11C 5/02 20130101; H04L
67/34 20130101; H04Q 11/0071 20130101; H05K 7/1487 20130101; H05K
5/0204 20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; H04L 12/24 20060101 H04L012/24 |
Claims
1. A system, comprising: a pod management controller coupled with
sleds via an out-of-band (OOB) network, the pod management
controller to: receive metric data from a plurality of management
controllers for the sleds via the OOB network, the sleds comprising
physical resources and the metric data to indicate one or more
metrics for the physical resources; determine a physical resource
of the physical resources to perform a task based on the one or
more metrics; and send the task to be performed by the physical
resource of one of the sleds.
2. The system of claim 1, the pod management controller to
determine the physical resource to perform the task based on the
metric data indicating the physical resource is capable of meeting
a requirement of a service level agreement associated with the
task.
3. The system of claim 1, the logic to receive the metric data from
the plurality of management controllers for the sleds located
within a single rack.
4. The system of claim 1, the logic to receive the metric data from
the plurality of management controllers for the sleds located
within two or more racks.
5. The system of claim 1, the logic to receive the metric data via
the OOB network utilizing a representational state transfer (REST)
architecture and in a JavaScript Object Notation (JSON) data
format.
6. The system of claim 1, the physical resources comprising one or
more physical memory resource and the metric data for each of the
physical memory resources comprising one or more of an indication
whether physical memory resources are interleaved or
non-interleaved, one or more of a memory throughput, a memory
input/output operations per second (IOPS) metric, a memory latency,
a memory size, and a memory utilization.
7. The system of claim 1, the physical resources comprising one or
more physical compute resource and the metric data for each of the
physical compute resources comprising one or more of a processor
identifier, a processor cache capability, a processor topology, a
processor cache topology, processor-to-processor link access
latency, and processor bandwidth information.
8. The system of claim 1, the physical resources comprising one or
more physical storage resources and the metric data for each of the
one or more physical storage resource comprising one or more of a
storage throughput, a storage input/output operations per second
(IOPS) metric, a storage latency, a storage size, and a storage
utilization.
9. The system of claim 1, the pod management controller to receive
the metric data via a rack management controller receiving metric
data from the sleds of one or more racks.
10. A non-transitory computer-readable storage medium, comprising a
plurality of instructions, that when executed, enable processing
circuitry to: receive, by a pod management controller, metric data
from a plurality of management controllers for sleds via an
out-of-band (OOB) network, the sleds comprising physical resources
and the metric data to indicate one or more metrics for the
physical resources; determine, by the pod management controller, a
physical resource of the physical resources to perform a task based
at least in part on the one or more metrics; and send, by the pod
management controller, the task to be performed by the physical
resource of one of the sleds.
11. The computer-readable storage medium of claim 10, comprising a
plurality of instructions, that when executed, enable processing
circuitry to determine the physical resource to perform the task
based on the metric data indicating the physical resource is
capable of meeting a requirement of a service level agreement
associated with the task.
12. The computer-readable storage medium of claim 10, comprising a
plurality of instructions, that when executed, enable processing
circuitry to receive the metric data from the plurality of
management controllers for the sleds located within a single
rack.
13. The computer-readable storage medium of claim 10, comprising a
plurality of instructions, that when executed, enable processing
circuitry to receive the metric data from the plurality of
management controllers for the sleds located within two or more
racks.
14. The computer-readable storage medium of claim 10, comprising a
plurality of instructions, that when executed, enable processing
circuitry to receive the metric data via the OOB network utilizing
a representational state transfer (REST) architecture and in a
JavaScript Object Notation (JSON) data format.
15. The computer-readable storage medium of claim 10, the physical
resources comprising one or more physical memory resource and the
metric data for each of the physical memory resources comprising
one or more of an indication whether physical memory resources are
interleaved or non-interleaved, one or more of a memory throughput,
a memory input/output operations per second (IOPS) metric, a memory
latency, a memory size, and a memory utilization.
16. The computer-readable storage medium of claim 10, the physical
resources comprising one or more physical compute resource and the
metric data for each of the physical compute resources comprising
one or more of a processor identifier, a processor cache
capability, a processor topology, a processor cache topology,
processor-to-processor link access latency, and processor bandwidth
information.
17. The computer-readable storage medium of claim 10, the physical
resources comprising one or more physical storage resources and the
metric data for each of the one or more physical storage resource
comprising one or more of a storage throughput, a storage
input/output operations per second (IOPS) metric, a storage
latency, a storage size, and a storage utilization.
18. The computer-readable storage medium of claim 10, comprising a
plurality of instructions, that when executed, enable processing
circuitry to receive the metric data via a rack management
controller receiving metric data from a plurality of sleds of one
or more racks.
19. An apparatus, comprising: a management controller of a sled
coupled with a pod management controller via an out-of-band (OOB)
link, the management controller to: determine metric data for one
or more physical resources of the sled, the metric data to indicate
one or more metrics for the one or more physical resources; send
the metric data to a pod management controller via the OOB link;
and receive a task to be processed by at least one of the one or
more physical resources of the sled.
20. The apparatus of claim 19, the logic to send the metric data
via the OOB link utilizing a representational state transfer (REST)
architecture and in a JavaScript Object Notation (JSON) data
format.
21. The apparatus of claim 19, the physical resources comprising
one or more physical memory resource and the metric data for each
of the physical memory resources comprising one or more of an
indication whether physical memory resources are interleaved or
non-interleaved, one or more of a memory throughput, a memory
input/output operations per second (IOPS) metric, a memory latency,
a memory size, and a memory utilization.
22. The apparatus of claim 19, the physical resources comprising
one or more physical compute resource and the metric data for each
of the physical compute resources comprising one or more of a
processor identifier, a processor cache capability, a processor
topology, a processor cache topology, processor-to-processor link
access latency, and processor bandwidth information.
23. The apparatus of claim 19, the physical resources comprising
one or more physical storage resources and the metric data for each
of the one or more physical storage resources comprising one or
more of a storage throughput, a storage input/output operations per
second (IOPS) metric, a storage latency, a storage size, and a
storage utilization.
24. The apparatus of claim 19, the management controller to send
the metric data to the pod management controller via a rack
management controller.
25. The apparatus of claim 19, the physical resources comprising
one or more of a physical memory resource, a physical compute
resource, a physical storage resource, and a physical accelerator
resource.
Description
RELATED CASES
[0001] This application claims priority to U.S. Provisional Patent
Application No. 62/365,969, filed Jul. 22, 2016, U.S. Provisional
Patent Application No. 62/376,859, filed Aug. 18, 2016, and U.S.
Provisional Patent Application No. 62/427,268, filed Nov. 29, 2016,
each of which are hereby incorporated by reference in their
entirety.
TECHNICAL FIELD
[0002] Embodiments described herein generally include determining
and communicating metric for physical resources in a data center
environment.
BACKGROUND
[0003] A computing data center may include one or more computing
systems including a plurality of compute nodes that may include
various compute structures (e.g., servers or sleds) and may be
physically located on multiple racks. The sleds may include a
number of physical resources interconnected via one or more compute
structures and buses. Moreover, the sleds may be interconnected
with other sleds via networking connections.
[0004] Typically, a computing data center may include a management
entity to distribute workloads among the compute structures located
within the racks. However, these compute structures currently fail
to provide the management entity detailed system information
containing performance related information, such that the
management entity may make intelligent decisions when providing the
workloads.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] Embodiments of the invention are illustrated by way of
example, and not by way of limitation, in the figures of the
accompanying drawings in which like reference numerals refer to
similar elements.
[0006] FIG. 1 illustrates an example of a data center.
[0007] FIG. 2 illustrates an example of a rack.
[0008] FIG. 3 illustrates an example of a data center.
[0009] FIG. 4 illustrates an example of a data center.
[0010] FIG. 5 illustrates an example of a switching
infrastructure.
[0011] FIG. 6 illustrates an example of a data center.
[0012] FIG. 7 illustrates an example of a sled.
[0013] FIG. 8 illustrates an example of a data center.
[0014] FIG. 9 illustrates an example of a data center.
[0015] FIG. 10 illustrates an example of a sled.
[0016] FIG. 11 illustrates an example of a data center.
[0017] FIG. 12 illustrates an example of a data center.
[0018] FIG. 13 illustrates an example of a data center.
[0019] FIG. 14 illustrates an example of a data center.
[0020] FIG. 15 illustrates an example of a sled.
[0021] FIG. 16 illustrates an example of a data center.
[0022] FIG. 17 illustrates an example of a first logic flow
diagram.
[0023] FIG. 18 illustrates an example of a second logic flow
diagram.
DETAILED DESCRIPTION
[0024] Various embodiments may be generally directed to determining
metric data for a number of physical resources in a data center
environment and providing the metric data such that a management
controller can make intelligent decisions when allocating the
workloads to the physical resources. As previously mentioned,
current compute structures fail to provide management controllers
detailed system information containing performance metrics for the
compute structures such that management controllers may make
intelligent decisions when providing the workloads. Thus,
embodiments discussed herein are directed to solving these other
problems.
[0025] For example, embodiments discussed herein may include
circuitry to determine metric data for one or more physical
resources of a sled, the metric data to indicate one or more
metrics for the one or more physical resources. The circuitry may
also send the metric data to a pod management controller via an
out-of-band (OOB) link, which may include a physical link or a
virtual link, and secure. The metric data may include performance
metrics and additional information such that the pod management
controller may make intelligent decisions to process workloads by
the physical resources. Moreover, the pod management controller may
receive metric data from a plurality sleds via the OOB network,
determine a physical resource of the physical resources to perform
a task based at least in part on the one or more metrics, and cause
the task to be performed by the physical resource. Embodiments are
not limited in this manner and these other details will become
apparent in the following discussion.
[0026] Reference is now made to the drawings, wherein like
reference numerals are used to refer to like elements throughout.
In the following description, for purposes of explanation, numerous
specific details are set forth in order to provide a thorough
understanding thereof. It may be evident, however, that the novel
embodiments can be practiced without these specific details. In
other instances, well-known structures and devices are shown in
block diagram form in order to facilitate a description thereof.
The intention is to cover all modifications, equivalents, and
alternatives consistent with the claimed subject matter.
[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 are designed 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 twister 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 usage information for the various resources, predicts
resource usage for different types of workloads based on past
resource usage, 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-manipulable resource sleds. Furthermore, in the
illustrative embodiment, the racks 102A, 102B, 102C, 102D include
integrated power sources that receive higher current than typical
for power sources. The increased current enables the power sources
to provide additional power to the components on each sled,
enabling the components to operate at higher than typical
frequencies. 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 204-3, and physical compute resources 205-5
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 manipulable sleds comprising disaggregated resources,
each type of resource can be upgraded independently of each other
and at their own optimized refresh rate.
[0031] FIG. 3 illustrates an example of a data center 300 that may
generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. In the particular non-limiting example depicted in
FIG. 3, data center 300 comprises racks 302-1 to 302-32. In various
embodiments, the racks of data center 300 may be arranged in such
fashion as to define and/or accommodate various access pathways.
For example, as shown in FIG. 3, the racks of data center 300 may
be arranged in such fashion as to define and/or accommodate access
pathways 311A, 311B, 311C, and 311D. In some embodiments, the
presence of such access pathways may generally enable automated
maintenance equipment, such as robotic maintenance equipment, to
physically access the computing equipment housed in the various
racks of data center 300 and perform automated maintenance tasks
(e.g., replace a failed sled, upgrade a sled). In various
embodiments, the dimensions of access pathways 311A, 311B, 311C,
and 311D, the dimensions of racks 302-1 to 302-32, and/or one or
more other aspects of the physical layout of data center 300 may be
selected to facilitate such automated operations. The embodiments
are not limited in this context.
[0032] FIG. 4 illustrates an example of a data center 400 that may
generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. As shown in FIG. 4, data center 400 may feature an
optical fabric 412. Optical fabric 412 may generally comprise a
combination of optical signaling media (such as optical cabling)
and optical switching infrastructure via which any particular sled
in data center 400 can send signals to (and receive signals from)
each of the other sleds in data center 400. The signaling
connectivity that optical fabric 412 provides to any given sled may
include connectivity both to other sleds in a same rack and sleds
in other racks. In the particular non-limiting example depicted in
FIG. 4, data center 400 includes four racks 402A to 402D. Racks
402A to 402D house respective pairs of sleds 404A-1 and 404A-2,
404B-1 and 404B-2, 404C-1 and 404C-2, and 404D-1 and 404D-2. Thus,
in this example, data center 400 comprises a total of eight sleds.
Via optical fabric 412, each such sled may possess signaling
connectivity with each of the seven other sleds in data center 400.
For example, via optical fabric 412, sled 404A-1 in rack 402A may
possess signaling connectivity with sled 404A-2 in rack 402A, as
well as the six other sleds 404B-1, 404B-2, 404C-1, 404C-2, 404D-1,
and 404D-2 that are distributed among the other racks 402B, 402C,
and 402D of data center 400. The embodiments are not limited to
this example.
[0033] FIG. 5 illustrates an overview of a connectivity scheme 500
that may generally be representative of link-layer connectivity
that may be established in some embodiments among the various sleds
of a data center, such as any of example data centers 100, 300, and
400 of FIGS. 1, 3, and 4. Connectivity scheme 500 may be
implemented using an optical fabric that features a dual-mode
optical switching infrastructure 514. Dual-mode optical switching
infrastructure 514 may generally comprise a switching
infrastructure that is capable of receiving communications
according to multiple link-layer protocols via a same unified set
of optical signaling media, and properly switching such
communications. In various embodiments, dual-mode optical switching
infrastructure 514 may be implemented using one or more dual-mode
optical switches 515. In various embodiments, dual-mode optical
switches 515 may generally comprise high-radix switches. In some
embodiments, dual-mode optical switches 515 may comprise multi-ply
switches, such as four-ply switches. In various embodiments,
dual-mode optical switches 515 may feature integrated silicon
photonics that enable them to switch communications with
significantly reduced latency in comparison to conventional
switching devices. In embodiments, the dual-mode switch may be a
single physical network wire that may be capable of carrying
Ethernet or Onmi-Path communication, which may be auto-detected by
the dual-mode optical switch 515 or configured by the Pod
management controller. This allows for the same network to be used
for Cloud traffic (Ethernet) or High Performance Computing (HPC),
typically Onmi-Path or Infiniband. Moreover, and in some instances,
an Onmi-Path protocol may carry Onmi-Path communication and
Ethernet communication. 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. Note that in some embodiments, the architecture
may not be a leaf-spine architecture, but may be a two-ply switch
architecture to connect directly to the sleds.
[0034] In various embodiments, dual-mode optical switches may be
capable of receiving both Ethernet protocol communications carrying
Internet Protocol (IP packets) and communications according to a
second, high-performance computing (HPC) link-layer protocol (e.g.,
Intel's Omni-Path Architecture's, Infiniband) via optical signaling
media of an optical fabric. As reflected in FIG. 5, with respect to
any particular pair of sleds 504A and 504B possessing optical
signaling connectivity to the optical fabric, connectivity scheme
500 may thus provide support for link-layer connectivity via both
Ethernet links and HPC links. Thus, both Ethernet and HPC
communications can be supported by a single high-bandwidth,
low-latency switch fabric. The embodiments are not limited to this
example.
[0035] FIG. 6 illustrates a general overview of a rack architecture
600 that may be representative of an architecture of any particular
one of the racks depicted in FIGS. 1 to 4 according to some
embodiments. As reflected in FIG. 6, rack architecture 600 may
generally feature a plurality of sled spaces into which sleds may
be inserted, each of which may be robotically-accessible via a rack
access region 601. In the particular non-limiting example depicted
in FIG. 6, rack architecture 600 features five sled spaces 603-1 to
603-5. Sled spaces 603-1 to 603-5 feature respective multi-purpose
connector modules (MPCMs) 616-1 to 616-5. In some instances, when a
sled is inserted into any given one of sled spaces 603-1 to 603-5,
the corresponding MPCM may couple with a counterpart MPCM of the
inserted sled. This coupling may provide the inserted sled with
connectivity to both signaling infrastructure and power
infrastructure of the rack in which it is housed.
[0036] Included among the types of sleds to be accommodated by rack
architecture 600 may be one or more types of sleds that feature
expansion capabilities. FIG. 7 illustrates an example of a sled 704
that may be representative of a sled of such a type. As shown in
FIG. 7, sled 704 may comprise a set of physical resources 705, as
well as an MPCM 716 designed to couple with a counterpart MPCM when
sled 704 is inserted into a sled space such as any of sled spaces
603-1 to 603-5 of FIG. 6. Sled 704 may also feature an expansion
connector 717. Expansion connector 717 may generally comprise a
socket, slot, or other type of connection element that is capable
of accepting one or more types of expansion modules, such as an
expansion sled 718. By coupling with a counterpart connector on
expansion sled 718, expansion connector 717 may provide physical
resources 705 with access to supplemental computing resources 705B
residing on expansion sled 718. The embodiments are not limited in
this context.
[0037] FIG. 8 illustrates an example of a rack architecture 800
that may be representative of a rack architecture that may be
implemented in order to provide support for sleds featuring
expansion capabilities, such as sled 704 of FIG. 7. In the
particular non-limiting example depicted in FIG. 8, rack
architecture 800 includes seven sled spaces 803-1 to 803-7, which
feature respective MPCMs 816-1 to 816-7. Sled spaces 803-1 to 803-7
include respective primary regions 803-1A to 803-7A and respective
expansion regions 803-1B to 803-7B. With respect to each such sled
space, when the corresponding MPCM is coupled with a counterpart
MPCM of an inserted sled, the primary region may generally
constitute a region of the sled space that physically accommodates
the inserted sled. The expansion region may generally constitute a
region of the sled space that can physically accommodate an
expansion module, such as expansion sled 718 of FIG. 7, in the
event that the inserted sled is configured with such a module.
[0038] FIG. 9 illustrates an example of a rack 902 that may be
representative of a rack implemented according to rack architecture
800 of FIG. 8 according to some embodiments. In the particular
non-limiting example depicted in FIG. 9, rack 902 features seven
sled spaces 903-1 to 903-7, which include respective primary
regions 903-1A to 903-7A and respective expansion regions 903-1B to
903-7B. In various embodiments, temperature control in rack 902 may
be implemented using an air cooling system. For example, as
reflected in FIG. 9, rack 902 may feature a plurality of fans 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.).
[0039] MPCMs 916-1 to 916-7 may be configured to provide inserted
sleds with access to power sourced by respective power modules
920-1 to 920-7, each of which may draw power from an external power
source 921. 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.
[0040] MPCMs 916-1 to 916-7 may also be arranged to provide
inserted sleds with optical signaling connectivity to a dual-mode
optical switching infrastructure 914, which may be the same as--or
similar to--dual-mode optical switching infrastructure 514 of FIG.
5. In various embodiments, optical connectors contained in MPCMs
916-1 to 916-7 may be designed to couple with counterpart optical
connectors contained in MPCMs of inserted sleds to provide such
sleds with optical signaling connectivity to dual-mode optical
switching infrastructure 914 via respective lengths of optical
cabling 922-1 to 922-7. In some embodiments, each such length of
optical cabling may extend from its corresponding MPCM to an
optical interconnect loom 923 that is external to the sled spaces
of rack 902. In various embodiments, optical interconnect loom 923
may be arranged to pass through a support post or other type of
load-bearing element of rack 902. The embodiments are not limited
in this context. Because inserted sleds connect to an optical
switching infrastructure via MPCMs, the resources typically spent
in manually configuring the rack cabling to accommodate a newly
inserted sled can be saved.
[0041] FIG. 10 illustrates an example of a sled 1004 that may be
representative of a sled designed for use in conjunction with rack
902 of FIG. 9 according to some embodiments. Sled 1004 may feature
an MPCM 1016 that comprises an optical connector 1016A and a power
connector 1016B, and that is designed to couple with a counterpart
MPCM of a sled space in conjunction with insertion of MPCM 1016
into that sled space. Coupling MPCM 1016 with such a counterpart
MPCM may cause power connector 1016 to couple with a power
connector comprised in the counterpart MPCM. This may generally
enable physical resources 1005 of sled 1004 to source power from an
external source, via power connector 1016 and power transmission
media 1024 that conductively couples power connector 1016 to
physical resources 1005.
[0042] Sled 1004 may also include dual-mode optical network
interface circuitry 1026. Dual-mode optical network interface
circuitry 1026 may generally comprise circuitry that is capable of
communicating over optical signaling media according to each of
multiple link-layer protocols supported by dual-mode optical
switching infrastructure 914 of FIG. 9. In some embodiments,
dual-mode optical network interface circuitry 1026 may be capable
both of Ethernet protocol communications and of communications
according to a second, high-performance protocol. In various
embodiments, dual-mode optical network interface circuitry 1026 may
include one or more optical transceiver modules 1027, each of which
may be capable of transmitting and receiving optical signals over
each of one or more optical channels. The embodiments are not
limited in this context.
[0043] Coupling MPCM 1016 with a counterpart MPCM of a sled space
in a given rack may cause optical connector 1016A to couple with an
optical connector comprised in the counterpart MPCM. This may
generally establish optical connectivity between optical cabling of
the sled and dual-mode optical network interface circuitry 1026,
via each of a set of optical channels 1025. Dual-mode optical
network interface circuitry 1026 may communicate with the physical
resources 1005 of sled 1004 via electrical signaling media 1028. In
addition to the dimensions of the sleds and arrangement of
components on the sleds to provide improved cooling and enable
operation at a relatively higher thermal envelope (e.g., 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.
[0044] FIG. 11 illustrates an example of a data center 1100 that
may generally be representative of one in/for which one or more
techniques described herein may be implemented according to various
embodiments. As reflected in FIG. 11, a physical infrastructure
management framework 1150A may be implemented to facilitate
management of a physical infrastructure 1100A of data center 1100.
In various embodiments, one function of physical infrastructure
management framework 1150A may be to manage automated maintenance
functions within data center 1100, such as the use of robotic
maintenance equipment to service computing equipment within
physical infrastructure 1100A. In some embodiments, physical
infrastructure 1100A may feature an advanced telemetry system that
performs telemetry reporting that is sufficiently robust to support
remote automated management of physical infrastructure 1100A. In
various embodiments, telemetry information provided by such an
advanced telemetry system may support features such as failure
prediction/prevention capabilities and capacity planning
capabilities. In some embodiments, physical infrastructure
management framework 1150A may also be configured to manage
authentication of physical infrastructure components using hardware
attestation techniques. For example, robots may verify the
authenticity of components before installation by analyzing
information collected from a radio frequency identification (RFID)
tag associated with each component to be installed. The embodiments
are not limited in this context.
[0045] As shown in FIG. 11, the physical infrastructure 1100A of
data center 1100 may comprise an optical fabric 1112, which may
include a dual-mode optical switching infrastructure 1114. Optical
fabric 1112 and dual-mode optical switching infrastructure 1114 may
be the same as--or similar to--optical fabric 412 of FIG. 4 and
dual-mode optical switching infrastructure 514 of FIG. 5,
respectively, and may provide high-bandwidth, low-latency,
multi-protocol connectivity among sleds of data center 1100. As
discussed above, with reference to FIG. 1, in various embodiments,
the availability of such connectivity may make it feasible to
disaggregate and dynamically pool resources such as accelerators,
memory, and storage. In some embodiments, for example, one or more
pooled accelerator sleds 1130 may be included among the physical
infrastructure 1100A of data center 1100, each of which may
comprise a pool of accelerator resources--such as co-processors
and/or FPGAs, for example--that is available globally accessible to
other sleds via optical fabric 1112 and dual-mode optical switching
infrastructure 1114.
[0046] In another example, in various embodiments, one or more
pooled storage sleds 1132 may be included among the physical
infrastructure 1100A of data center 1100, each of which may
comprise a pool of storage resources that is available 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.
[0047] In various embodiments, one or more layers of abstraction
may be applied to the physical resources of physical infrastructure
1100A in order to define a virtual infrastructure, such as a
software-defined infrastructure 1100B. In some embodiments, virtual
computing resources 1136 of software-defined infrastructure 1100B
may be allocated to support the provision of cloud services 1140.
In various embodiments, particular sets of virtual computing
resources 1136 may be grouped for provision to cloud services 1140
in the form of SDI services 1138. Examples of cloud services 1140
may include--without limitation--software as a service (SaaS)
services 1142, platform as a service (PaaS) services 1144, and
infrastructure as a service (IaaS) services 1146.
[0048] In some embodiments, management of software-defined
infrastructure 1100B may be conducted using a virtual
infrastructure management framework 1150B. In various embodiments,
virtual infrastructure management framework 1150B may be designed
to implement workload fingerprinting techniques and/or
machine-learning techniques in conjunction with managing allocation
of virtual computing resources 1136 and/or SDI services 1138 to
cloud services 1140. In some embodiments, virtual infrastructure
management framework 1150B may use/consult telemetry data in
conjunction with performing such resource allocation. In various
embodiments, an application/service management framework 1150C may
be implemented in order to provide QoS management capabilities for
cloud services 1140. The embodiments are not limited in this
context.
[0049] FIG. 12 illustrates an example of a data center 1200 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. 12, the data center 1200 may be similar to and
include features and components previously discussed. For example,
the data center 1200 may generally contain a plurality of racks
1202A to 1202D, each of which may house computing equipment
including a respective set of physical resources 1205A-x to
1205D-x, where x may be any positive integer from 1 to 4. The
physical resources 1205 may be contained within a number of sleds
1204A through 1204D. As mentioned, the physical resources 1205 may
include resources of multiple types, such as--for
example--processors, co-processors, fully-programmable gate arrays
(FPGAs), memory, accelerators, and storage. In embodiments, the
physical resources 1205 may include physical compute resources,
physical memory resources, physical storage resources, and physical
accelerator resources.
[0050] In embodiments, the physical resources 1205 may be pooled
within racks and between racks. For example, physical resources
1205A-1 of sled 1204A-1 may be pooled with physical resources
1205A-3 of sled 1204A-3 to provide combined processing capabilities
for workloads across sleds within the same rack, e.g. rack 1202A.
Similarly, physical resources of one or more racks may be combined
with physical resources of one or more other racks to create a pool
of physical resources to process a workload. In one example, the
physical resources 1205A-3 may be combined and pooled with physical
resources of 1205B-1, which are located within rack 1202A and rack
102B, respectively. Any combination of physical resources 1205 may
be pooled to process a workload and embodiments are not limited in
this manner. Moreover, some embodiments may include more or less
physical resources 1205, sleds 1204, and/or racks 1202 and the
illustrated example should not be construed in a limiting
manner.
[0051] In the illustrated example of FIG. 12, the data center 1200
may provide management functionality to monitor the physical
resources 1205 and provide intelligent workload and processing
capabilities. The intelligent workload capabilities may include,
but are not limited to, collecting metric data for the physical
resources 1205, determining processing for one or more tasks of a
workload by the physical resources 1205, and causing the one or
more tasks of the workload to be processed by one or more
particular physical resource(s) 1205 based on the metric data and
service level agreement requirements.
[0052] To perform these capabilities, embodiments include
communicating low-level metric data for the physical resources 1205
to a pod management controller 1231. Moreover, the data center 1200
includes a pod management controller 1231 to provide management
functionality. The pod management controller 1231 may be
implemented in circuitry and logic and be part of a pod management
system. The pod management controller 1231 may provide a set of
application programming interfaces (API) to enable operations
operating on the sleds 1204 and racks 1202 to utilize the
management functionality.
[0053] In embodiments, the pod management controller 1231 may
couple with one or more racks 1202 via one or more Ethernet links
as part of an out-of-band (OOB) network. In one example, the OOB
network may be a separate network from an optical fiber network
used to communicate data between the sleds, for example. Moreover,
the OOB network may be a dedicated network to communicate
management and control data between the sleds 1204, racks 1202, and
the pod management controller 1231. In some instances, the OOB
network may support other protocols and technology to communicate
metric data, such as Infiniband, Onmi-Path, and so forth. These
communications may include the metric data for the physical
resources 1205 for use in predicting usage of the physical
resources 1205 and determining the allocation of the physical
resources 1205 for processing current and future workloads. These
and other details will become more apparent with the following
description.
[0054] FIG. 13 illustrates an example of a data center management
architecture 1300 that may 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. The data center management architecture 1300 of FIG.
13 illustrates a rack 1302 having a number of sleds 1304-1 through
1304-n, where n may be any positive integer. Note that FIG. 13 only
illustrates a single rack 1302 having sleds 130-n coupled with a
pod management system 1333. However, embodiments are not limited in
this manner, as previously discussed above in FIG. 12, for
example.
[0055] Each of the sleds 1304-n includes a number of components,
including physical resources 1305, a management controller 1362,
and Ethernet (ETH) circuitry 1352. As will be discussed in more
detail with respect to FIG. 15, each sled 1304 may also include an
MPCM having an ETH connector to couple with a corresponding ETH
connector to enable communication via the Ethernet links.
[0056] In embodiments, the ETH circuitry 1352 may enable
communication via one or more Ethernet links of an OOB network. In
some embodiments, the ETH circuitry 1352 may enable gigabyte
communications with other devices, such as a pod management system
1333. The ETH circuitry 1352 may be a media-independent interface,
which may use any network signal transmission media. The
media-independent interface may be a reduced media-independent
interface (RMII), gigabit media-independent interface (GMII),
reduced gigabit media-independent interface (RGMII), 10-gigabit
media-independent interface (XGMII) and serial gigabit
media-independent interface (SGMII), for example.
[0057] In embodiments, the ETH circuitry 1352 may support
communications via one or more architectures and data structures.
For example, the ETH circuitry 1352 is capable of communicating
data utilizing a representational state transfer (REST)
architecture and in a JavaScript Object Notation (JSON) data
format. In one example, the ETH circuitry 1352 may support a set of
APIs and schema, such as Redfish.RTM., to enable communication of
data between the sleds 1304, the racks 1302, and the pod management
controller 1331. In embodiments, the ETH circuitry 1352 may be
coupled with and include memory to store the functions required to
operate in accordance with the REST architecture and communicate
data in JSON data format. For example, the ETH circuitry 1352 may
be coupled with a 256 megabytes (MBs) of error correction control
(ECC) memory to run an embedded operation system, e.g. Linux, to
provide the ETH interface (Redfish.RTM.). Embodiments are not
limited in this manner and other web services architectures may be
utilized to communicate the metric data and be consistent with
embodiments discussed herein.
[0058] In embodiments, each of the sleds 1304-n may also include a
management controller 1362-n to collect and determine metric data
for the physical resources 1305-n. A management controller 1362
also provides management functionality including sending the metric
data in a data structure to a pod management controller 1331. In
some instances, the management controller 1362 may be part of an
Intelligent Platform Management Interface (IPMI) architecture and
may be a baseboard management controller (BMC) or specialized
service processor that monitors the physical state and operational
state of the physical resources 1305 using sensors and
communicating with the physical resources 1305 themselves. In some
instances, the management controller 1362 may be a sled management
controller. Embodiments are not limited in this manner. For
example, in some embodiments, the metric data may also be
communicated to the pod management controller 1331 as new original
equipment manufacturer (OEM) records in the system management basic
input/output (SMBIOS) records.
[0059] The management controller 1362 may collect metric data such
as temperature, humidity, power-supply voltage, fan speeds,
communication parameters and operating system functions. If metrics
associated with these variables are determined to be out of
specification or do not meet one or more service level agreement
requirements, the management controller 1362 may notify or send the
metric data to the pod management controller 1331. Moreover, and in
some embodiments, the metric data may be reported or sent to the
pod management controller 1331 on a periodic or semi-periodic
basis, even when the variables are not out of specification.
Embodiments are not limited in this manner.
[0060] In some embodiments, the management controller 1362 may
collect metric data specific to a type of physical resource 1305.
For example, a physical resource 1305 may be a physical memory
resource and the management controller 1362 may collect metric
data, such as a number of memory channels, a memory bandwidth, a
memory size, a memory type, read/write parameters, a memory speed,
read/write latency, partitioning information, high bandwidth memory
size, socket interconnect latency, interleaving or non-interleaving
indication, whether two-level memory (2LM) is utilized, near and
far memory type of 2LM, size of 2LM, performance of 2LM region and
so forth. Note that embodiments may also include multi-level
memory, e.g. 3LM, wherein near memory (first layer) may be
connected via a physical interconnect on the same baseboard and far
memory may be connected via a fiber interconnect and be in another
separate sled. The separate may include volatile memory (second
layer), and 3D XPoint memory (third layer), for example. some
embodiments, the management controller 1362 may provide advanced
configuration and power interface (ACPI) static resource affinity
table (SRAT) information and hierarchical memory attribute table
(HMAT) information. via the OOB network. Typically, these low-level
metrics associated with physical memory resources is not provided
to the pod management controller 1331. Thus, by providing this
low-level metric data to the pod management controller 1331, the
pod management controller 1331 may make more intelligent decisions
when placing tasks for workloads for processing.
[0061] In another example, the management controller 1362 may
provide metric data for physical compute resources or processor,
such as a processor identifier, a processor cache capability, a
processor topology, a processor cache topology,
processor-to-processor latency, bandwidth information, performance
(speed/throughput) metrics, and so forth. In a third example, a
physical resource 1305 may be a physical storage resource and the
management controller 1362 may collect metric data such as a
storage throughput, a storage input/output operations per second
(IOPS) metric, a storage latency, a storage size, and a storage
utilization. Embodiments are not limited to these examples. For
instance, a physical resource 1305 may be an accelerator resource
and accelerator-related metrics may be determined for the
accelerator, such as a card link width associated with a Peripheral
Component Interconnect Express (PCIe) card, a solid-state drive
(SSD), a field programmable gate array (FPGA) card, and so
forth.
[0062] In embodiments, the management controller 1362 may collect
and determine the metric data for each of the physical resources
1305 utilizing any number of methods or techniques. For example,
the management controller 1362 may include sensors to detect metric
data itself. In another example, the management controller 1362 may
determine the metric data based on sensors and determinations made
by the physical resources 1305 themselves. More specifically, the
physical resources 1305 may be able to monitor such metrics as
throughput, usage, processing time, read/write speeds, etc. and
communicate the metric data to the management controller 1362. The
physical resources 1305 may also store metric data about itself
within a memory location, fuse logic, or so forth that may be
polled by the management controller 1362 to collect the metric
data. For example, memory resource may store an indication of
memory size, memory type, memory channels, memory bandwidth, etc. A
processor resource may store similar information, such as processor
speed, processor topology, processor type, and so forth. Similar
metrics may also be polled by a management controller 1362 with
respect to storage resources, such as storage size, storage
throughput (read/write times), storage type, and so forth.
Embodiments are not limited in this manner and the management
controller 1362 may collect metric data via other means including
receiving or retrieving information from an operating system.
[0063] The management controller 1362 may collect and determine the
metric data on a periodic or semi-periodic basis. In some
instances, the metric data may be collected on a
periodic/semi-periodic basis based on a user setting, a factory
(time of manufacturer) setting, service level agreement
requirements, and so forth. Embodiments are not limited in this
manner.
[0064] The management controller 1362 may be capable of
communicating via one or more different interface or bus types to
collect the metric data. For example, the management controller
1362 may be coupled with the one or more physical resources via a
low pin count (LPC) bus, a system management bus (SMBus), an
Inter-Integrated (I2C) bus, an IPMI utilizing the SMBus, and a
serial port. These interfaces and buses may be used to collect and
determine metric data from the different physical resources.
[0065] The management controller 1362 may also be coupled with the
ETH circuitry 1352 via one or more buses/interfaces to communicate
the metric data with the pod management controller 1331. More
specifically, the management controller 1362 may utilize the ETH
circuitry 1352 to communicate the metric data via a REST
architecture and in a JSON data format, for example.
[0066] FIG. 14 illustrates an example of a data center management
architecture 1400 that may 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. The data center management architecture 1400 of FIG.
14 may be similar to the data center management architecture 1300
illustrated in FIG. 13; however, the rack 1402 may include a rack
management controller 1464 to communicate the metric data with the
sleds 1404 and the pod management controller 1431. For example, the
rack management controller 1464 may collect (or receive) the metric
data from the sleds 1404 and send the metric data to the pod
management system 1433 and pod management controller 1431. In a
data center management architecture 1400, each rack, such as rack
1402, may include a rack management controller 1464 to collect
metric data and other information from each of the sleds 1404.
[0067] In embodiments, the rack management controller 1464 may
include instructions and logic, such as Intel's.RTM. Pooled System
Management Engine (PSME), to collect, manage, and communicate the
metric data for each of the sleds 1404 in the rack 1402. The rack
management controller 1464 may also include ETH circuitry 1456,
which may be similar to the ETH circuitry 1452 of the sleds 1404.
For example, the ETH circuitry 1456 may be a RGMII interface and
capable of communicating data utilizing a REST Architecture in a
JSON data structure, such as Redfish.RTM.. In some instances, the
rack management controller 1464 may operate as a server including a
REST API to gather the metric data from the sleds 1405 and
present/send the metric data to the pod management controller 1431.
The sleds 1405, rack management controller 1464, and the pod
management controller 1431 may communicate the metric data through
a JSON-RPC as a transport and JSON as a data structure, for
example. Moreover and as similarly discussed above, these
communications may be made in an OOB network environment to prevent
interference and bandwidth usage with other data. Embodiments are
not limited in this manner.
[0068] FIG. 15 illustrates an example of a sled 1504 that may be
representative of a sled designed for use in conjunction with the
racks discussed herein, for example. In embodiments, sled 1504 may
be similar to and have similar components and functionality as sled
1004 discussed in FIG. 15. Sled 1504 may feature an MPCM 1516 that
which may include an optical connector 1516A, a power connector
1516B, and an ETH connector 1516C, and that is designed to couple
with a counterpart MPCM of a sled space in conjunction with
insertion of MPCM 1516 into that sled space. Coupling MPCM 1516
with such a counterpart MPCM may cause power connector 1516B to
couple with a power connector comprised in the counterpart MPCM.
This may enable physical resources 1505 of sled 1504 to source
power from an external source, via power connector 1516B and power
transmission media 1524 that conductively couples power connector
1516 to physical resources 1505.
[0069] Sled 1504 may also include dual-mode optical network
interface circuitry 1526. Dual-mode optical network interface
circuitry 1526 may include 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, as previously discussed in FIGS. 9 and
10. In some embodiments, dual-mode optical network interface
circuitry 1526 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 1526 may include one or more
optical transceiver modules 1527, 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.
[0070] Coupling MPCM 1516 with a counterpart MPCM of a sled space
in a given rack may cause optical connector 1516A to couple with an
optical connector comprised in the counterpart MPCM. This may
establish optical connectivity between optical cabling of the sled
and dual-mode optical network interface circuitry 1526, via each of
a set of optical channels 1525. Dual-mode optical network interface
circuitry 1526 may communicate with the physical resources 1505 of
sled 1504 via electrical signaling media 1528.
[0071] The sled 1504 may also include a management controller 1562,
which may be the same as or similar to management controller 1362
of FIG. 13 and management controller 1462 of FIG. 14. The
management controller 1562 may determine and collect metric data
for physical resources 1505, including but not limited to, physical
memory resources 1505-1, physical compute resources 1505-2,
physical storage resources 1505-3, and physical accelerator
resources 1505-4. Embodiments are not limited in this manner.
[0072] A physical memory resource 1505-1 may be any type of memory,
such as any machine-readable or computer-readable media capable of
storing data, including both volatile and non-volatile memory. In
some embodiments, the machine-readable or computer-readable medium
may include a non-transitory medium. Moreover, a physical memory
resource 1505-1 may include one or more higher speed memory units,
such as read-only memory (ROM), random-access memory (RAM), dynamic
RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM
(SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable
programmable ROM (EPROM), electrically erasable programmable ROM
(EEPROM), flash memory, polymer memory such as ferroelectric
polymer memory, ovonic memory, phase change or ferroelectric
memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory,
magnetic or optical cards, an array of devices such as Redundant
Array of Independent Disks (RAID) drives, solid state memory
devices (e.g., USB memory, solid state drives (SSD), 3D
Xpoint.RTM., and any other type of storage media suitable for
storing information. Embodiments are not limited to these
examples.
[0073] A physical compute resource 1505-2 may be any type of
circuitry capable of processing information. Moreover, a physical
compute resources 1505-2 may be implemented using any processor or
logic device. The physical compute resource 1505-2 may be one or
more of any type of computational element, such as but not limited
to, a microprocessor, a processor, central processing unit, digital
signal processing unit, dual core processor, mobile device
processor, desktop processor, single core processor, a
system-on-chip (SoC) device, complex instruction set computing
(CISC) microprocessor, a reduced instruction set (RISC)
microprocessor, a very long instruction word (VLIW) microprocessor,
or any other type of processor or processing circuit on a single
chip or integrated circuit. The physical compute resource 1505-2
may be connected to and communicate with the other physical
resources 1505 of the computing system via an interconnect, such as
one or more buses, control lines, and data lines.
[0074] In embodiments, a physical storage resource 1505-3 may be
any type of storage, and may be implemented as a non-volatile
storage device such as, but not limited to, a magnetic disk drive,
optical disk drive, tape drive, an internal storage device, an
attached storage device, flash memory, battery backed-up SDRAM
(synchronous DRAM), and/or a network accessible storage device. In
embodiments, a physical storage resource 1505-3 may include
technology to increase the storage performance enhanced protection
for valuable digital media when multiple hard drives are included,
for example. Further examples of physical storage resource 1505-3
may include a hard disk, floppy disk, Compact Disk Read Only Memory
(CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Rewriteable
(CD-RW), optical disk, magnetic media, magneto-optical media,
removable memory cards or disks, various types of DVD devices, a
tape device, a cassette device, or the like. The embodiments are
not limited in this context.
[0075] A physical accelerator resource 1505-4 may be any type of
accelerator device designed to increase processing power of a
processor, such as the physical compute resource 1505-2. The
physical accelerator resource 1505-4 accelerates transmission or
processing beyond processor capabilities. In one example, a
physical accelerator resource 1505-4 may compute faster
floating-point units (FPUs) by assisting in math calculations or by
increasing speed. In another example, the physical accelerator
resource 1505-4 may be a graphics processing units (GPUs) for 3-D
images or faster graphic displays. Embodiments, the physical
accelerator resource 1505-4 may be implemented as field
programmable gate arrays (FPGAs); however, embodiments are not
limited in this manner.
[0076] The management controller 1562 may collect metric data for
one or more of the physical resources 1505 via one or more
interconnects 1538 and electrical signals. The interconnects 1538
may be a low pin count (LPC) bus, a system management bus (SMBus),
an Inter-Integrated (I2C) bus, an IPMI utilizing the SMBus, and a
serial port. Embodiments are not limited to these examples.
[0077] In embodiments, the management controller 1562 may
communicate the metric data to a pod management controller. In some
instances, the management controller 1562 may communicate the
metric data to the pod management controller via a rack management
controller. To send the metric data, the management controller 1562
may utilize the ETH circuitry 1552 to send the metric data via a
REST architecture and in a JSON data format, as previously
discussed. The ETH circuitry 1552 may provide a layered
architecture to communicate the metric in the REST architecture and
a JSON data format, for example. The management controller 1562 may
send the metric data via one or more interconnects 1538 as
electrical signals, for example.
[0078] The ETH circuitry 1552 may process the metric data to
communicate it via the REST architecture in a JSON data format. For
example, the ETH circuitry 1552 may put the metric data in one or
more packets having a JSON data format. The ETH circuitry 1552 may
send the metric data via the ETH connector 1515C coupled with a
counterpart ETH connector in a sled space in conjunction with
insertion of MPCM 1515 into that sled space. In some instances, the
ETH connector 1515C may be a modular connector or uniquely designed
connector to couple with the counterpart ETH connector in the sled
space. In one example, the ETH connector 1515C may have the same
wiring pinout as a registered jack 45 (RJ45) modular connector.
However, the ETH connector 1515C may be designed differently than a
standard RJ45 connector such that it may couple with the
counterpart ETH connector in the sled space.
[0079] In some embodiments, the management controller 1562 and ETH
circuitry 1552 may perform a serial-to-local area network (LAN)
conversion to communicate the metric data to the pod management
controller. For example, the management controller 1562 may collect
the metric data via one or more serial links, convert the data for
transmission into packets in a JSON data format, and communicate
the metric data to the pod management controller. Embodiments are
not limited to these examples.
[0080] FIG. 16 illustrates an example of a data center system 1600
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. 16, the data center system 1600 may be similar to and
include features and components previously discussed. For example,
the data center system 1600 may generally contain a plurality of
racks 1602A to 1602D, each of which may house computing equipment
including a respective set of physical resources 1605A-x to
1605D-x, where x may be any positive integer from 1 to 4. The
physical resources 1605 may be contained within a number of sleds
1604A through 1604D. As mentioned, the physical resources 1605 may
include resources of multiple types, such as--for
example--processors, co-processors, fully-programmable gate arrays
(FPGAs), memory, accelerators, and storage.
[0081] In embodiments, the sleds 1604 may communicate metric data
to a pod management controller 1631, as previously discussed. For
example, the sleds 1604 may each include a management controller
(not shown) that may collect the metric data of the physical
resources 1605 and send the metric data to the pod management
controller 1631 either directly or via a rack management controller
(not shown). Moreover, the metric data may be sent to the pod
management controller 1631 via an OOB network on one or more ETH
links utilizing a REST architecture and in a JSON data format.
[0082] The pod management controller 1631 utilize the metric data
to determine physical resources 1605 for processing one or more
tasks of a workload. For example, the pod management controller
1631 may implement an orchestration layer, such as OpenStack.RTM.,
to consume the metric data to allocate physical resources for
processing workloads. In embodiments, the pod management controller
1631 may utilize the metric data in combination with service level
agreement (SLA) requirements to cause tasks for workloads to be
processed by physical resources while maintaining the requirements
stipulated in a SLA. The SLA may be based on a policy-based storage
management system to help evaluate and maintain an adequate level
performance for a data center. The SLA may specify a set of one or
more values or metrics relating to one or more specific, measurable
performance characteristics and specifying one or more desired or
required levels of service to be provided to a workload including
one or more tasks. Some requirements may include, latency, cost,
protection against local failures or corruption, geographic
dispersion, efficiency, throughput, processing times, etc. Thus,
SLA requirements can be defined regarding any one or more of these
characteristics, and other characteristics. By collecting the
metric data and determine actual performance relative to SLA, it
can be determined whether a data center is performing adequately,
and adjustments to the state of the data center system can be made
if it is not. For example, the pod management controller 1631 may
adjust, send, cause, etc. which physical resources are processing
particular tasks of workloads to ensure that the requirements of
the SLA are being met. More specifically, processing cycles on
physical compute resources, memory read/writes of physical memory
resources, data storage of physical storage resources, and
processing cycles of accelerators may be allocated to workloads
based on the SLA requirements and metric data.
[0083] In embodiments, the pod management controller 1631 may
determine SLA requirements from data stored in a memory or storage,
such as data store 1677. The SLA requirements may be stored in the
data store 1677 based on user input or computer determinations
specifying particular SLA requirements for workloads. Thus, a pod
management controller 1631 may receive an indication of a workload
to be processed by the data center 1600 from one or more clients
1679. The pod management controller 1631 can determine the SLA
requirements for the workload based on the data in the data store
1677. For example, the pod management controller 1631 may perform a
lookup and retrieve the SLA requirements for the workload based on
an identifier identifying the workload.
[0084] The pod management controller 1631 may utilize the SLA
requirements for the workload and the metric data received from the
racks 1602 to determine which physical resources 1605 are to
process one or more tasks of the workload. For example, the pod
management controller 1631 may determine which physical resources
1605 are capable to process one or more tasks of a workload to meet
SLA requirements for the workload. The pod management controller
1631 may cause the one or more tasks to be processed by the
determined physical resources 1605. Note that the metric data may
be communicated between the racks 1602 and the pod management
controller 1631 via the OOB network; however, the one or more tasks
may be communicated to the racks 1602 and particular resources 1605
via a different network, such as an optical fiber network.
Embodiments are not limited in this manner.
[0085] FIG. 17 illustrates an embodiment of logic flow 1700. The
logic flow 1700 may be representative of some or all of the
operations executed by one or more embodiments described herein.
For example, the logic flow 1700 may illustrate operations
performed by a pod management controller, as discussed herein.
However, embodiments are not limited in this, and one or more
operations may be performed by other components or systems
discussed herein.
[0086] At block 1702, the logic flow 1700 includes to determining
metric data for one or more physical resources. As previously
discussed, a pod management controller may receive metric data from
a management controller of a sled and a rack management controller
of a rack having one or more sleds. The metric data may be
collected and determined by a management controller of a sled
having physical resources and provided via an OOB network.
[0087] At block 1704, the logic flow 1700 includes determining one
or more tasks of workload that are to be processed by a data
center. In some instances, a pod management controller may receive
the tasks and workload or an indication of the task and workload.
The indication may identify tasks and workload, for example. The
tasks may include any type of operations, jobs, and processing that
may be completed by the physical resources. For example, a task
includes instructions to be processed by a physical compute
resource, read/write requests for a physical memory resource,
read/write requests for a physical storage resource and
instructions to be processed by a physical accelerator
resource.
[0088] At block 1706, the logic flow 1700 includes determining one
or more SLA requirements for the workload. For example, a pod
management controller may retrieve SLA requirement data from data
associated with the workload. The SLA requirements may specify one
or more requirements for the workload and the tasks, such as
processing, throughput, IOPS, read/write speeds, etc. Embodiments
are not limited in this manner.
[0089] At block 1708, the logic flow 1700 may determine one or more
physical resources to process one or more tasks for a workload. For
example, a pod management controller may determine which physical
resources are capable of processing the tasks while meeting the SLA
requirements for the tasks. Note that the pod management controller
may utilize a single physical resource to perform a task or pool
two more physical resources to process the task. In embodiments,
the pod management controller may determine which physical
resources based on the metric data indicating which of the physical
resources can meet the SLA requirements.
[0090] At block 1710, the logic flow 1700 includes causing a task
to be performed by the one or more physical resources determine to
be capable of meeting the SLA requirement for the task. For
example, a pod management controller may communicate information to
one or more clients or other systems indicating which physical
resources are to perform/process one or more task of a workload.
Embodiments are not limited in this manner.
[0091] Although logic flow 1700 illustrates particular operations
occurring in a particular order, embodiments are not limited in
this manner and some operations may occur before, after or during
other operations. Also, logic flow 1700 may repeat any number of
times and embodiments are not limited in this manner.
[0092] FIG. 18 illustrates an embodiment of logic flow 1800. The
logic flow 1800 may be representative of some or all of the
operations executed by one or more embodiments described herein.
For example, the logic flow 1800 may illustrate operations
performed by a management controller of a sled, as discussed
herein. However, embodiments are not limited in this, and one or
more operations may be performed by other components or systems
discussed herein.
[0093] At block 1802, the logic flow 1800 includes determining
metric data for one or more physical resources of a sled. For
example, a management controller may determine and collect metric
data for one or more physical resources including, but not limited
to, a physical memory resource, a physical compute resource, a
physical storage resource, and a physical accelerator resource. The
management controller may collect the metric data via one or more
sensors, from the physical resources, and from an operating system
for a sled. Embodiments are not limited in this manner.
[0094] At block 1804, the logic flow 1800 includes sending the
metric data to a pod management controller. In some instances, a
management controller may send the metric data directly to the pod
management controller in a REST architecture in a JSON data format
via an OOB network. In another example, the management controller
may send the metric data to the pod management controller via a
rack management controller. A rack management controller may
receive metric data from any number of sleds and physical resources
of sleds to send to the pod management controller. The rack
management controller may also send the metric in a REST
architecture and JSON data format. Note that in both examples, the
metric data may be sent to the pod management controller via an OOB
network such that the metric data does not interfere with
processing and data transfer on other networks, such as an optical
fiber network.
[0095] At block 1806, the logic flow 1800 may include receiving a
task to be processed by one or more physical resources of a sled.
In some instances, the task may be part of workload being processed
by the data center and may be sent to the sled having the one or
more physical resources based on the metric data. Embodiments are
not limited in this manner.
[0096] The detailed disclosure now turns to providing examples that
pertain to further embodiments. Examples one through twenty-five
(1-25) provided below are intended to be exemplary and
non-limiting.
[0097] In a first example, a system, a device, an apparatus, and so
forth may include a pod management controller receive metric data
from a plurality of management controllers for sleds via an
out-of-band (OOB) network, the sleds comprising physical resources
and the metric data to indicate one or more metrics for the
physical resources, determine a physical resource of the physical
resources to perform a task based at least in part on the one or
more metrics, and cause the task to be performed by the physical
resource.
[0098] In a second example and in furtherance of the first example,
a system, a device, an apparatus, and so forth including the pod
management controller to determine the physical resource to perform
the task based on the metric data indicating the physical resource
is capable of meeting a requirement of a service level agreement
associated with the task.
[0099] In a third example and in furtherance of any of the previous
examples, a system, a device, an apparatus, and so forth including
the pod management controller to receive the metric data from the
plurality of management controllers for the sleds located within a
single rack.
[0100] In a fourth example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including the logic to receive the metric data from the plurality
of management controllers for the sleds located within two or more
racks.
[0101] In a fifth example and in furtherance of any of the previous
examples, a system, a device, an apparatus, and so forth including
the pod management controller to receive the metric data via the
OOB network utilizing a representational state transfer (REST)
architecture and in a JavaScript Object Notation (JSON) data
format.
[0102] In a sixth example and in furtherance of any of the previous
examples, a system, a device, an apparatus, and so forth including
the pod management controller to cause processing of the physical
resources comprising one or more physical memory resource and the
metric data for each of the physical memory resources comprising
the physical resources comprising one or more physical memory
resource and the metric data for each of the physical memory
resources comprising one or more of an indication whether the
physical memory resources are interleaved or non-interleaved, one
or more of a memory throughput, a memory input/output operations
per second (IOPS) metric, a memory latency, a memory size, and a
memory utilization.
[0103] In a seventh example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including the pod management controller to cause processing of the
physical resources comprising one or more physical compute resource
and the metric data for each of the physical compute resources
comprising one or more of a processor identifier, a processor cache
capability, a processor topology, a processor cache topology,
processor-to-processor link access latency, and processor bandwidth
information.
[0104] In an eighth example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including the pod management controller to cause processing of the
physical resources comprising one or more physical storage
resources and the metric data for each of the one or more physical
storage resource comprising one or more of a storage throughput, a
storage input/output operations per second (IOPS) metric, a storage
latency, a storage size, and a storage utilization.
[0105] In a ninth example and in furtherance of any of the previous
examples, a system, a device, an apparatus, and so forth including
the pod management controller to receive the metric data via a rack
management controller receiving metric data from a plurality of
sleds of one or more racks.
[0106] In a tenth example and in furtherance of any of the previous
examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
receive metric data from a plurality of management controllers for
sleds via an out-of-band (OOB) network, the sleds comprising
physical resources and the metric data to indicate one or more
metrics for the physical resources, determine a physical resource
of the physical resources to perform a task based at least in part
on the one or more metrics, and cause the task to be performed by
the physical resource.
[0107] In an eleventh example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
determine the physical resource to perform the task based on the
metric data indicating the physical resource is capable of meeting
a requirement of a service level agreement associated with the
task.
[0108] In a twelfth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
receive the metric data from the plurality of management
controllers for the sleds located within a single rack.
[0109] In a thirteenth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
receive the metric data from the plurality of management
controllers for the sleds located within two or more racks.
[0110] In a fourteenth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
receive the metric data via the OOB network utilizing a
representational state transfer (REST) architecture and in a
JavaScript Object Notation (JSON) data format.
[0111] In a fifteenth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
receive the metric data via a rack management controller receiving
metric data from a plurality of sleds of one or more racks.
[0112] In a sixteenth example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including a management controller to determine metric data for one
or more physical resources of a sled, the metric data to indicate
one or more metrics for the one or more physical resources, send
the metric data to a pod management controller via an out-of-band
(OOB) link, receive a task for processing by one or more of the
physical resources.
[0113] In a seventeenth example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including the management controller to send the metric data via the
OOB link utilizing a representational state transfer (REST)
architecture and in a JavaScript Object Notation (JSON) data
format.
[0114] In an eighteenth example and in furtherance of any of the
previous examples, a system, a device, an apparatus, and so forth
including the management controller to send the metric data to the
pod management controller via a rack management controller.
[0115] In a nineteenth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
determine metric data for one or more physical resources of a sled,
the metric data to indicate one or more metrics for the one or more
physical resources, and send the metric data to a pod management
controller via an out-of-band (OOB) link.
[0116] In a twentieth example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
send the metric data via the OOB link utilizing a representational
state transfer (REST) architecture and in a JavaScript Object
Notation (JSON) data format.
[0117] In a twenty-first example and in furtherance of any of the
previous examples, embodiments may include a non-transitory
computer-readable storage medium, comprising a plurality of
instructions, that when executed, enable processing circuitry to
send the metric data to the pod management controller via a rack
management controller.
[0118] In a twenty-second example and in furtherance of any of the
previous examples, embodiments may include one or more methods to
perform any combination of the above-recited examples or other
methods/logic flows discussed herein.
[0119] Some embodiments may be described using the expression "one
embodiment" or "an embodiment" along with their derivatives. These
terms mean that a particular feature, structure, or characteristic
described in connection with the embodiment is included in at least
one embodiment. The appearances of the phrase "in one embodiment"
in various places in the specification are not necessarily all
referring to the same embodiment. Further, some embodiments may be
described using the expression "coupled" and "connected" along with
their derivatives. These terms are not necessarily intended as
synonyms for each other. For example, some embodiments may be
described using the terms "connected" and "coupled" to indicate
that two or more elements are in direct physical or electrical
contact with each other. The term "coupled," however, may also mean
that two or more elements are not in direct contact with each
other, but yet still co-operate or interact with each other.
[0120] It is emphasized that the Abstract of the Disclosure is
provided to allow a reader to quickly ascertain the nature of the
technical disclosure. It is submitted with the understanding that
it will not be used to interpret or limit the scope or meaning of
the claims. Also, in the preceding Detailed Description, it can be
seen that various features are grouped together in a single
embodiment for the purpose of streamlining the disclosure. This
method of disclosure is not to be interpreted as reflecting an
intention that the claimed embodiments require more features than
are expressly recited in each claim. Rather, as the following
claims reflect, inventive subject matter lies in less than all
features of a single disclosed embodiment. Thus the following
claims are at this moment incorporated into the Detailed
Description, with each claim standing on its own as a separate
embodiment. In the appended claims, the terms "including" and "in
which" are used as the plain-English equivalents of the respective
terms "comprising" and "wherein," respectively. Moreover, the terms
"first," "second," "third," and so forth, are used merely as
labels, and are not intended to impose numerical requirements on
their objects.
[0121] What has been described above includes examples of the
disclosed architecture. It is, of course, not possible to describe
every conceivable combination of components and/or methodologies,
but one of ordinary skill in the art may recognize that many
further combinations and permutations are possible. Accordingly,
the novel architecture is intended to embrace all such alterations,
modifications and variations that fall within the spirit and scope
of the appended claims.
* * * * *