Patent Application
20190250857
A compute device may execute multiple different workloads. Some workloads may be executed by or on behalf of multiple different tenants. Different workloads may benefit from different cache quality of service (QoS) settings. Typical computing devices require a tenant to manually configure cache QoS settings for each workload.
The concepts described herein are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. Where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements.
While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.
References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).
The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).
In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.
Referring now to
A data center comprising disaggregated resources, such as data center 100, can be used in a wide variety of contexts, such as enterprise, government, cloud service provider, and communications service provider (e.g., Telco's), as well in a wide variety of sizes, from cloud service provider mega-data centers that consume over 100,000 sq. ft. to single- or multi-rack installations for use in base stations.
The disaggregation of resources to sleds comprised predominantly of a single type of resource (e.g., compute sleds comprising primarily compute resources, memory sleds containing primarily memory resources), and the selective allocation and deallocation of the disaggregated resources to form a managed node assigned to execute a workload improves the operation and resource usage of the data center 100 relative to typical data centers comprised of hyperconverged servers containing compute, memory, storage and perhaps additional resources in a single chassis. For example, because sleds predominantly contain resources of a particular type, resources of a given type can be upgraded independently of other resources. Additionally, because different resources types (processors, storage, accelerators, etc.) typically have different refresh rates, greater resource utilization and reduced total cost of ownership may be achieved. For example, a data center operator can upgrade the processors throughout their facility by only swapping out the compute sleds. In such a case, accelerator and storage resources may not be contemporaneously upgraded and, rather, may be allowed to continue operating until those resources are scheduled for their own refresh. Resource utilization may also increase. For example, if managed nodes are composed based on requirements of the workloads that will be running on them, resources within a node are more likely to be fully utilized. Such utilization may allow for more managed nodes to run in a data center with a given set of resources, or for a data center expected to run a given set of workloads, to be built using fewer resources.
Referring now to
It should be appreciated that each of the other pods 120, 130, 140 (as well as any additional pods of the data center 100) may be similarly structured as, and have components similar to, the pod 110 shown in and described in regard to
Referring now to
In the illustrative embodiments, each sled of the data center 100 is embodied as a chassis-less sled. That is, each sled has a chassis-less circuit board substrate on which physical resources (e.g., processors, memory, accelerators, storage, etc.) are mounted as discussed in more detail below. As such, the rack 240 is configured to receive the chassis-less sleds. For example, each pair 310 of elongated support arms 312 defines a sled slot 320 of the rack 240, which is configured to receive a corresponding chassis-less sled. To do so, each illustrative elongated support arm 312 includes a circuit board guide 330 configured to receive the chassis-less circuit board substrate of the sled. Each circuit board guide 330 is secured to, or otherwise mounted to, a top side 332 of the corresponding elongated support arm 312. For example, in the illustrative embodiment, each circuit board guide 330 is mounted at a distal end of the corresponding elongated support arm 312 relative to the corresponding elongated support post 302, 304. For clarity of the Figures, not every circuit board guide 330 may be referenced in each Figure.
Each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 configured to receive the chassis-less circuit board substrate of a sled 400 when the sled 400 is received in the corresponding sled slot 320 of the rack 240. To do so, as shown in
It should be appreciated that each circuit board guide 330 is dual sided. That is, each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 on each side of the circuit board guide 330. In this way, each circuit board guide 330 can support a chassis-less circuit board substrate on either side. As such, a single additional elongated support post may be added to the rack 240 to turn the rack 240 into a two-rack solution that can hold twice as many sled slots 320 as shown in
In some embodiments, various interconnects may be routed upwardly or downwardly through the elongated support posts 302, 304. To facilitate such routing, each elongated support post 302, 304 includes an inner wall that defines an inner chamber in which interconnects may be located. The interconnects routed through the elongated support posts 302, 304 may be embodied as any type of interconnects including, but not limited to, data or communication interconnects to provide communication connections to each sled slot 320, power interconnects to provide power to each sled slot 320, and/or other types of interconnects.
The rack 240, in the illustrative embodiment, includes a support platform on which a corresponding optical data connector (not shown) is mounted. Each optical data connector is associated with a corresponding sled slot 320 and is configured to mate with an optical data connector of a corresponding sled 400 when the sled 400 is received in the corresponding sled slot 320. In some embodiments, optical connections between components (e.g., sleds, racks, and switches) in the data center 100 are made with a blind mate optical connection. For example, a door on each cable may prevent dust from contaminating the fiber inside the cable. In the process of connecting to a blind mate optical connector mechanism, the door is pushed open when the end of the cable approaches or enters the connector mechanism. Subsequently, the optical fiber inside the cable may enter a gel within the connector mechanism and the optical fiber of one cable comes into contact with the optical fiber of another cable within the gel inside the connector mechanism.
The illustrative rack 240 also includes a fan array 370 coupled to the cross-support arms of the rack 240. The fan array 370 includes one or more rows of cooling fans 372, which are aligned in a horizontal line between the elongated support posts 302, 304. In the illustrative embodiment, the fan array 370 includes a row of cooling fans 372 for each sled slot 320 of the rack 240. As discussed above, each sled 400 does not include any on-board cooling system in the illustrative embodiment and, as such, the fan array 370 provides cooling for each sled 400 received in the rack 240. Each rack 240, in the illustrative embodiment, also includes a power supply associated with each sled slot 320. Each power supply is secured to one of the elongated support arms 312 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320. For example, the rack 240 may include a power supply coupled or secured to each elongated support arm 312 extending from the elongated support post 302. Each power supply includes a power connector configured to mate with a power connector of the sled 400 when the sled 400 is received in the corresponding sled slot 320. In the illustrative embodiment, the sled 400 does not include any on-board power supply and, as such, the power supplies provided in the rack 240 supply power to corresponding sleds 400 when mounted to the rack 240. Each power supply is configured to satisfy the power requirements for its associated sled, which can vary from sled to sled. Additionally, the power supplies provided in the rack 240 can operate independent of each other. That is, within a single rack, a first power supply providing power to a compute sled can provide power levels that are different than power levels supplied by a second power supply providing power to an accelerator sled. The power supplies may be controllable at the sled level or rack level, and may be controlled locally by components on the associated sled or remotely, such as by another sled or an orchestrator.
Referring now to
As discussed above, the illustrative sled 400 includes a chassis-less circuit board substrate 602, which supports various physical resources (e.g., electrical components) mounted thereon. It should be appreciated that the circuit board substrate 602 is “chassis-less” in that the sled 400 does not include a housing or enclosure. Rather, the chassis-less circuit board substrate 602 is open to the local environment. The chassis-less circuit board substrate 602 may be formed from any material capable of supporting the various electrical components mounted thereon. For example, in an illustrative embodiment, the chassis-less circuit board substrate 602 is formed from an FR-4 glass-reinforced epoxy laminate material. Of course, other materials may be used to form the chassis-less circuit board substrate 602 in other embodiments.
As discussed in more detail below, the chassis-less circuit board substrate 602 includes multiple features that improve the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602. As discussed, the chassis-less circuit board substrate 602 does not include a housing or enclosure, which may improve the airflow over the electrical components of the sled 400 by reducing those structures that may inhibit air flow. For example, because the chassis-less circuit board substrate 602 is not positioned in an individual housing or enclosure, there is no vertically-arranged backplane (e.g., a backplate of the chassis) attached to the chassis-less circuit board substrate 602, which could inhibit air flow across the electrical components. Additionally, the chassis-less circuit board substrate 602 has a geometric shape configured to reduce the length of the airflow path across the electrical components mounted to the chassis-less circuit board substrate 602. For example, the illustrative chassis-less circuit board substrate 602 has a width 604 that is greater than a depth 606 of the chassis-less circuit board substrate 602. In one particular embodiment, for example, the chassis-less circuit board substrate 602 has a width of about 21 inches and a depth of about 9 inches, compared to a typical server that has a width of about 17 inches and a depth of about 39 inches. As such, an airflow path 608 that extends from a front edge 610 of the chassis-less circuit board substrate 602 toward a rear edge 612 has a shorter distance relative to typical servers, which may improve the thermal cooling characteristics of the sled 400. Furthermore, although not illustrated in
As discussed above, the illustrative sled 400 includes one or more physical resources 620 mounted to a top side 650 of the chassis-less circuit board substrate 602. Although two physical resources 620 are shown in
The sled 400 also includes one or more additional physical resources 630 mounted to the top side 650 of the chassis-less circuit board substrate 602. In the illustrative embodiment, the additional physical resources include a network interface controller (NIC) as discussed in more detail below. Of course, depending on the type and functionality of the sled 400, the physical resources 630 may include additional or other electrical components, circuits, and/or devices in other embodiments.
The physical resources 620 are communicatively coupled to the physical resources 630 via an input/output (I/O) subsystem 622. The I/O subsystem 622 may be embodied as circuitry and/or components to facilitate input/output operations with the physical resources 620, the physical resources 630, and/or other components of the sled 400. For example, the I/O subsystem 622 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, waveguides, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In the illustrative embodiment, the I/O subsystem 622 is embodied as, or otherwise includes, a double data rate 4 (DDR4) data bus or a DDR5 data bus.
In some embodiments, the sled 400 may also include a resource-to-resource interconnect 624. The resource-to-resource interconnect 624 may be embodied as any type of communication interconnect capable of facilitating resource-to-resource communications. In the illustrative embodiment, the resource-to-resource interconnect 624 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the resource-to-resource interconnect 624 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to resource-to-resource communications.
The sled 400 also includes a power connector 640 configured to mate with a corresponding power connector of the rack 240 when the sled 400 is mounted in the corresponding rack 240. The sled 400 receives power from a power supply of the rack 240 via the power connector 640 to supply power to the various electrical components of the sled 400. That is, the sled 400 does not include any local power supply (i.e., an on-board power supply) to provide power to the electrical components of the sled 400. The exclusion of a local or on-board power supply facilitates the reduction in the overall footprint of the chassis-less circuit board substrate 602, which may increase the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602 as discussed above. In some embodiments, voltage regulators are placed on a bottom side 750 (see
In some embodiments, the sled 400 may also include mounting features 642 configured to mate with a mounting arm, or other structure, of a robot to facilitate the placement of the sled 600 in a rack 240 by the robot. The mounting features 642 may be embodied as any type of physical structures that allow the robot to grasp the sled 400 without damaging the chassis-less circuit board substrate 602 or the electrical components mounted thereto. For example, in some embodiments, the mounting features 642 may be embodied as non-conductive pads attached to the chassis-less circuit board substrate 602. In other embodiments, the mounting features may be embodied as brackets, braces, or other similar structures attached to the chassis-less circuit board substrate 602. The particular number, shape, size, and/or make-up of the mounting feature 642 may depend on the design of the robot configured to manage the sled 400.
Referring now to
The memory devices 720 may be embodied as any type of memory device capable of storing data for the physical resources 620 during operation of the sled 400, such as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as dynamic random access memory (DRAM) or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM). In particular embodiments, DRAM of a memory component may comply with a standard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4 for LPDDR4. Such standards (and similar standards) may be referred to as DDR-based standards and communication interfaces of the storage devices that implement such standards may be referred to as DDR-based interfaces.
In one embodiment, the memory device is a block addressable memory device, such as those based on NAND or NOR technologies. A memory device may also include next-generation nonvolatile devices, such as Intel 3D XPoint™ memory or other byte addressable write-in-place nonvolatile memory devices. In one embodiment, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology, resistive memory including the metal oxide base, the oxygen vacancy base and the conductive bridge Random Access Memory (CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magnetic junction memory based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristor based memory device, or a combination of any of the above, or other memory. The memory device may refer to the die itself and/or to a packaged memory product. In some embodiments, the memory device may comprise a transistor-less stackable cross point architecture in which memory cells sit at the intersection of word lines and bit lines and are individually addressable and in which bit storage is based on a change in bulk resistance.
Referring now to
In the illustrative compute sled 800, the physical resources 620 are embodied as processors 820. Although only two processors 820 are shown in
In some embodiments, the compute sled 800 may also include a processor-to-processor interconnect 842. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the processor-to-processor interconnect 842 may be embodied as any type of communication interconnect capable of facilitating processor-to-processor interconnect 842 communications. In the illustrative embodiment, the processor-to-processor interconnect 842 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the processor-to-processor interconnect 842 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.
The compute sled 800 also includes a communication circuit 830. The illustrative communication circuit 830 includes a network interface controller (NIC) 832, which may also be referred to as a host fabric interface (HFI). The NIC 832 may be embodied as, or otherwise include, any type of integrated circuit, discrete circuits, controller chips, chipsets, add-in-boards, daughtercards, network interface cards, or other devices that may be used by the compute sled 800 to connect with another compute device (e.g., with other sleds 400). In some embodiments, the NIC 832 may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors, or included on a multichip package that also contains one or more processors. In some embodiments, the NIC 832 may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC 832. In such embodiments, the local processor of the NIC 832 may be capable of performing one or more of the functions of the processors 820. Additionally or alternatively, in such embodiments, the local memory of the NIC 832 may be integrated into one or more components of the compute sled at the board level, socket level, chip level, and/or other levels.
The communication circuit 830 is communicatively coupled to an optical data connector 834. The optical data connector 834 is configured to mate with a corresponding optical data connector of the rack 240 when the compute sled 800 is mounted in the rack 240. Illustratively, the optical data connector 834 includes a plurality of optical fibers which lead from a mating surface of the optical data connector 834 to an optical transceiver 836. The optical transceiver 836 is configured to convert incoming optical signals from the rack-side optical data connector to electrical signals and to convert electrical signals to outgoing optical signals to the rack-side optical data connector. Although shown as forming part of the optical data connector 834 in the illustrative embodiment, the optical transceiver 836 may form a portion of the communication circuit 830 in other embodiments.
In some embodiments, the compute sled 800 may also include an expansion connector 840. In such embodiments, the expansion connector 840 is configured to mate with a corresponding connector of an expansion chassis-less circuit board substrate to provide additional physical resources to the compute sled 800. The additional physical resources may be used, for example, by the processors 820 during operation of the compute sled 800. The expansion chassis-less circuit board substrate may be substantially similar to the chassis-less circuit board substrate 602 discussed above and may include various electrical components mounted thereto. The particular electrical components mounted to the expansion chassis-less circuit board substrate may depend on the intended functionality of the expansion chassis-less circuit board substrate. For example, the expansion chassis-less circuit board substrate may provide additional compute resources, memory resources, and/or storage resources. As such, the additional physical resources of the expansion chassis-less circuit board substrate may include, but is not limited to, processors, memory devices, storage devices, and/or accelerator circuits including, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.
Referring now to
As discussed above, the individual processors 820 and communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. In the illustrative embodiment, the processors 820 and communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those physical resources are linearly in-line with others along the direction of the airflow path 608. It should be appreciated that, although the optical data connector 834 is in-line with the communication circuit 830, the optical data connector 834 produces no or nominal heat during operation.
The memory devices 720 of the compute sled 800 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the processors 820 located on the top side 650 via the I/O subsystem 622. Because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the processors 820 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Of course, each processor 820 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments. Alternatively, in other embodiments, each processor 820 may be communicatively coupled to each memory device 720. In some embodiments, the memory devices 720 may be mounted to one or more memory mezzanines on the bottom side of the chassis-less circuit board substrate 602 and may interconnect with a corresponding processor 820 through a ball-grid array.
Each of the processors 820 includes a heatsink 850 secured thereto. Due to the mounting of the memory devices 720 to the bottom side 750 of the chassis-less circuit board substrate 602 (as well as the vertical spacing of the sleds 400 in the corresponding rack 240), the top side 650 of the chassis-less circuit board substrate 602 includes additional “free” area or space that facilitates the use of heatsinks 850 having a larger size relative to traditional heatsinks used in typical servers. Additionally, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602, none of the processor heatsinks 850 include cooling fans attached thereto. That is, each of the heatsinks 850 is embodied as a fan-less heatsink. In some embodiments, the heat sinks 850 mounted atop the processors 820 may overlap with the heat sink attached to the communication circuit 830 in the direction of the airflow path 608 due to their increased size, as illustratively suggested by
Referring now to
In the illustrative accelerator sled 1000, the physical resources 620 are embodied as accelerator circuits 1020. Although only two accelerator circuits 1020 are shown in
In some embodiments, the accelerator sled 1000 may also include an accelerator-to-accelerator interconnect 1042. Similar to the resource-to-resource interconnect 624 of the sled 600 discussed above, the accelerator-to-accelerator interconnect 1042 may be embodied as any type of communication interconnect capable of facilitating accelerator-to-accelerator communications. In the illustrative embodiment, the accelerator-to-accelerator interconnect 1042 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the accelerator-to-accelerator interconnect 1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. In some embodiments, the accelerator circuits 1020 may be daisy-chained with a primary accelerator circuit 1020 connected to the NIC 832 and memory 720 through the I/O subsystem 622 and a secondary accelerator circuit 1020 connected to the NIC 832 and memory 720 through a primary accelerator circuit 1020.
Referring now to
Referring now to
In the illustrative storage sled 1200, the physical resources 620 are embodied as storage controllers 1220. Although only two storage controllers 1220 are shown in
In some embodiments, the storage sled 1200 may also include a controller-to-controller interconnect 1242. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1242 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1242 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.
Referring now to
The storage cage 1252 illustratively includes sixteen mounting slots 1256 and is capable of mounting and storing sixteen solid state drives 1254. Of course, the storage cage 1252 may be configured to store additional or fewer solid state drives 1254 in other embodiments. Additionally, in the illustrative embodiment, the solid state drivers are mounted vertically in the storage cage 1252, but may be mounted in the storage cage 1252 in a different orientation in other embodiments. Each solid state drive 1254 may be embodied as any type of data storage device capable of storing long term data. To do so, the solid state drives 1254 may include volatile and non-volatile memory devices discussed above.
As shown in
As discussed above, the individual storage controllers 1220 and the communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. For example, the storage controllers 1220 and the communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those electrical components are linearly in-line with each other along the direction of the airflow path 608.
The memory devices 720 of the storage sled 1200 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the storage controllers 1220 located on the top side 650 via the I/O subsystem 622. Again, because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the storage controllers 1220 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Each of the storage controllers 1220 includes a heatsink 1270 secured thereto. As discussed above, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602 of the storage sled 1200, none of the heatsinks 1270 include cooling fans attached thereto. That is, each of the heatsinks 1270 is embodied as a fan-less heatsink.
Referring now to
In the illustrative memory sled 1400, the physical resources 620 are embodied as memory controllers 1420. Although only two memory controllers 1420 are shown in
In some embodiments, the memory sled 1400 may also include a controller-to-controller interconnect 1442. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1442 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1442 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. As such, in some embodiments, a memory controller 1420 may access, through the controller-to-controller interconnect 1442, memory that is within the memory set 1432 associated with another memory controller 1420. In some embodiments, a scalable memory controller is made of multiple smaller memory controllers, referred to herein as “chiplets”, on a memory sled (e.g., the memory sled 1400). The chiplets may be interconnected (e.g., using EMIB (Embedded Multi-Die Interconnect Bridge)). The combined chiplet memory controller may scale up to a relatively large number of memory controllers and I/O ports, (e.g., up to 16 memory channels). In some embodiments, the memory controllers 1420 may implement a memory interleave (e.g., one memory address is mapped to the memory set 1430, the next memory address is mapped to the memory set 1432, and the third address is mapped to the memory set 1430, etc.). The interleaving may be managed within the memory controllers 1420, or from CPU sockets (e.g., of the compute sled 800) across network links to the memory sets 1430, 1432, and may improve the latency associated with performing memory access operations as compared to accessing contiguous memory addresses from the same memory device.
Further, in some embodiments, the memory sled 1400 may be connected to one or more other sleds 400 (e.g., in the same rack 240 or an adjacent rack 240) through a waveguide, using the waveguide connector 1480. In the illustrative embodiment, the waveguides are 64 millimeter waveguides that provide 16 Rx (i.e., receive) lanes and 16 Tx (i.e., transmit) lanes. Each lane, in the illustrative embodiment, is either 16 GHz or 32 GHz. In other embodiments, the frequencies may be different. Using a waveguide may provide high throughput access to the memory pool (e.g., the memory sets 1430, 1432) to another sled (e.g., a sled 400 in the same rack 240 or an adjacent rack 240 as the memory sled 1400) without adding to the load on the optical data connector 834.
Referring now to
Additionally, in some embodiments, the orchestrator server 1520 may identify trends in the resource utilization of the workload (e.g., the application 1532), such as by identifying phases of execution (e.g., time periods in which different operations, each having different resource utilizations characteristics, are performed) of the workload (e.g., the application 1532) and pre-emptively identifying available resources in the data center 100 and allocating them to the managed node 1570 (e.g., within a predefined time period of the associated phase beginning). In some embodiments, the orchestrator server 1520 may model performance based on various latencies and a distribution scheme to place workloads among compute sleds and other resources (e.g., accelerator sleds, memory sleds, storage sleds) in the data center 100. For example, the orchestrator server 1520 may utilize a model that accounts for the performance of resources on the sleds 400 (e.g., FPGA performance, memory access latency, etc.) and the performance (e.g., congestion, latency, bandwidth) of the path through the network to the resource (e.g., FPGA). As such, the orchestrator server 1520 may determine which resource(s) should be used with which workloads based on the total latency associated with each potential resource available in the data center 100 (e.g., the latency associated with the performance of the resource itself in addition to the latency associated with the path through the network between the compute sled executing the workload and the sled 400 on which the resource is located).
In some embodiments, the orchestrator server 1520 may generate a map of heat generation in the data center 100 using telemetry data (e.g., temperatures, fan speeds, etc.) reported from the sleds 400 and allocate resources to managed nodes as a function of the map of heat generation and predicted heat generation associated with different workloads, to maintain a target temperature and heat distribution in the data center 100. Additionally or alternatively, in some embodiments, the orchestrator server 1520 may organize received telemetry data into a hierarchical model that is indicative of a relationship between the managed nodes (e.g., a spatial relationship such as the physical locations of the resources of the managed nodes within the data center 100 and/or a functional relationship, such as groupings of the managed nodes by the customers the managed nodes provide services for, the types of functions typically performed by the managed nodes, managed nodes that typically share or exchange workloads among each other, etc.). Based on differences in the physical locations and resources in the managed nodes, a given workload may exhibit different resource utilizations (e.g., cause a different internal temperature, use a different percentage of processor or memory capacity) across the resources of different managed nodes. The orchestrator server 1520 may determine the differences based on the telemetry data stored in the hierarchical model and factor the differences into a prediction of future resource utilization of a workload if the workload is reassigned from one managed node to another managed node, to accurately balance resource utilization in the data center 100.
To reduce the computational load on the orchestrator server 1520 and the data transfer load on the network, in some embodiments, the orchestrator server 1520 may send self-test information to the sleds 400 to enable each sled 400 to locally (e.g., on the sled 400) determine whether telemetry data generated by the sled 400 satisfies one or more conditions (e.g., an available capacity that satisfies a predefined threshold, a temperature that satisfies a predefined threshold, etc.). Each sled 400 may then report back a simplified result (e.g., yes or no) to the orchestrator server 1520, which the orchestrator server 1520 may utilize in determining the allocation of resources to managed nodes.
Referring now to
The computing device 1602 may be embodied as any type of device capable of performing the functions described herein. For example, the computing device 1602 may be embodied as, without limitation, a sled, a compute sled, an accelerator sled, a storage sled, a computer, a server, a distributed computing device, a disaggregated computing device, a laptop computer, a tablet computer, a notebook computer, a mobile computing device, a smartphone, a wearable computing device, a multiprocessor system, a server, a workstation, and/or a consumer electronic device. As shown in
The compute engine 1620 may be embodied as any type of compute engine capable of performing the functions described herein. For example, the compute engine 1620 may be embodied as a single or multi-core processor(s), digital signal processor, microcontroller, field-programmable gate array (FPGA), or other configurable circuitry, application-specific integrated circuit (ASIC), or other processor or processing/controlling circuit. Similarly, the memory 1624 may be embodied as any type of volatile, non-volatile, or persistent memory or data storage capable of performing the functions described herein. In operation, the memory 1624 may store various data and software used during operation of the computing device 1602 such as operating systems, applications, programs, libraries, and drivers. As shown, the memory 1624 may be communicatively coupled to the compute engine 1620 via the I/O subsystem 1622, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute engine 1620, the memory 1624, and other components of the computing device 1602. For example, the I/O subsystem 1622 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, sensor hubs, host controllers, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and/or other components and subsystems to facilitate the input/output operations. In some embodiments, the memory 1624 may be directly coupled to the compute engine 1620, for example via an integrated memory controller hub. Additionally, in some embodiments, the I/O subsystem 1622 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with the compute engine 1620, the memory 1624, and/or other components of the computing device 1602, on a single integrated circuit chip.
The data storage device 1626 may be embodied as any type of device or devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, non-volatile flash memory, 3D XPoint memory, persistent memory, or other data storage devices. The computing device 1602 may also include a communication subsystem 1628, which may be embodied as any network interface controller (NIC), communication circuit, device, or collection thereof, capable of enabling communications between the computing device 1602 and other remote devices over a computer network (not shown). The communication subsystem 1628 may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., Ethernet, Bluetooth®, Wi-Fi®, WiMAX, 3G, 4G LTE, etc.) to effect such communication.
Referring now to
The cache manager 1702 is configured to receive a data item associated with a workload. The data item may be embodied as, for example, a file, a block, a cache line, or other data item processed by the workload. The workload may be embodied as an application, a database, a virtual machine, or other workload executed by the computing device 1602. The cache manager 1702 may be further configured to receive an application hint associated with the data item. The cache manager 1702 is further configured to apply a cache QoS policy 1710 to the workload. The cache QoS policy 1710 may be embodied as, for example, a guaranteed or maximum bandwidth, a guaranteed or maximum I/O operations per second, a maximum latency, a caching mode, an amount of cache space allocated to the workload, or other cache QoS policy 1710.
The feature extractor 1704 is configured to extract a workload feature vector 1708 from the data item. Extracting the workload feature vector 1708 may include extracting the application hint associated with the data item. Extracting the feature vector 1708 may include parsing data content of the data item, for example identifying a media format of the data item, identifying a database format of the data item, identifying a sensor data format of the data item, or identifying a volume format or a filesystem format of the data item.
The workload analyzer 1706 is configured to determine a workload grouping based on the workload feature vector 1708 and then determine a cache QoS policy 1710 based on the workload grouping. The cache QoS policy 1710 is applied by the cache manager 1702 as described above. In some embodiments, the cache QoS policy 1710 may be determined by selecting the cache QoS policy 1710 based on a predetermined priority level associated with the workload grouping. In some embodiments, the cache QoS policy 1710 may be determined with a machine learning model based on the workload feature vector 1708.
Referring now to
In block 1810, the computing device 1602 extracts a workload feature vector 1708 from the data item. The feature vector 1708 may be embodied as an array, list, or other collection of attributes that may be indicative of the type of workload executed by the computing device 1602. The feature vector 1708 may include, for example, a detected application family, a detected application type, the application hint, an encoding, an encryption indicator, extracted header information and other extracted fields, detected data type, and/or other features extracted from the data item. One potential embodiment of a method for extracting the feature vector is described below in with
In block 1812, the computing device 1602 determines a workload grouping for the current workload based on the feature vector 1708. The workload grouping identifies a type of application, database, or other workload type that includes the current workload. For example, the workload grouping may identify the current workload as a database application, a streaming application, an operating system swap process, or other workload executed by the computing device 1602. In some embodiments, as described above, the workload grouping may identify the current workload on a more granular level, for example by identifying particular types of databases (e.g., SQLite, Oracle®, MySQL, etc.). The computing device 1602 may use a self-learning classification algorithm or other machine learning algorithm to determine the workload grouping based on the feature vector 1708. In some embodiments, in block 1814 the computing device 1602 may generate classification telemetry that is indicative of the workload grouping determination. A user (e.g., system administrator, tenant, or other user) may monitor the classification telemetry to analyze and/or modify workload groupings generated by the computing device 1602. For example, performance statistics may be gathered by the computing device 1602 and reported for each classified workload, which may reveal incorrect classifications or variants of the classifications that the computing device 1602 should be configured to recognize. In some embodiments, a user may override some or all workload groupings, manually group workloads that were not automatically recognized, or otherwise configure workload groupings based on the classification telemetry.
In block 1816, the computing device 1602 determines one or more cache QoS policies 1710 based on the workload grouping. The cache QoS policies 1710 may include guaranteed and/or maximum bandwidth thresholds (e.g., measured in megabytes per second), guaranteed and/or maximum thresholds on I/O operations per second (IOPs), guaranteed latency (e.g., measured in microseconds), may be in terms of burst versus average versus 90th or 99th percentile, and/or other QoS configuration settings. As another example, the cache QoS policies 1710 may include a caching mode selected based on the workload grouping. For example, if a Ceph reliable block device (RBD) volume is found to contain a boot partition, a swap partition, and an ext4 partition, the cache QoS policies 1710 could enable writeback caching of the swap and ext4 filesystems, and apply a larger flush delay to the swap partition. As another example, the cache QoS policies 1710 may allocate cache space for each workload differently. The cache QoS policies 1710 may be configured by an administrator of the computing device 1602 for a given volume or object based on a predetermined service level (e.g., a service level agreement).
In some embodiments, in block 1818, the computing device 1602 may determine the cache QoS policies 1710 based on one or more predetermined workload priorities. For example, certain workload groupings (e.g., database) may have a higher priority than other workloads and thus may be assigned relatively higher cache QoS policies 1710. In some embodiments, in block 1820, the computing device 1602 may determine the cache QoS policies 1710 based on one or more machine learning models. The computing device 1602 may input the feature vector 1708 into a trained model and receive the cache QoS policy 1710 as output.
In block 1822, the computing device 1602 applies the selected cache QoS policies 1710 to the current workload. After applying the cache QoS policies, the method 1800 loops back to block 1802, in which the computing device 1602 may continue monitoring data items and selecting cache QoS policies 1710.
Referring now to
In some embodiments, in block 1904, the computing device 1602 may extract a file extension, application hint, or other metadata indicative of the workload grouping. The extracted hints may be included in the file name associated with the data item (e.g., name/extension) or may be provided separately.
In some embodiments, in block 1906 the computing device 1602 parses the data item to extract one or more features. In some embodiments, in block 1908 the computing device 1602 may parse only a part of the data item, such as a predetermined number n of blocks, lines, or other segments of the data item. By parsing part of the data item, the computing device 1602 may improve performance as compared to parsing the entire data item while still extracting useful features for workload grouping. In some embodiments, in block 1910 the computing device 1602 may extract one or more data patterns or other workload type identifiers from the data item. For example, certain file types may begin with a predetermined string identifier, such as “% PDF-1.6” for PDF files, “<!DOCTYPE html>” for HTML files, or other known header signature. As another example, certain document and/or media formats such as document files, text files, source code files, binary files, machine image files, video files, image files, sensor data (e.g., LIDAR sensor data), and/or other media format files may include identifiable patterns, signatures, or other identifiable shared characteristics. In some embodiments, in block 1912, the computing device 1602 may extract one or more data headers or other metadata from the data item. For example, the computing device 1602 may extract metadata such as sender, recipients, or other addressing information from an email data item. As another example, the computing device 1602 may extract a text encoding (e.g., UTF-8, etc.) from a text file data item.
In block 1914, the computing device 1602 determines whether the application associated with the data item is known based on the feature vector 1708 extracted so far. In block 1916, the computing device 1602 checks whether the application is known. If the application is not known, the method 1900 branches to block 1918, in which the computing device 1602 adds an “unknown application” application feature to the feature vector 1708, and then the method 1900 advances to block 1922. Referring back to block 1916, if the application is known, the method 1900 branches to block 1920, in which the computing device 1602 adds application-specific features for the known application to the feature vector 1708. The computing device 1602 may add, for example, an application name, an application type, an application hint, or other features identifying the known application. As another example, the computing device 1602 may add additional application-specific data and/or metadata determined by parsing the data item. After adding the application-specific features, the method 1900 branches to block 1922.
In block 1922, the computing device 1602 determines whether the data item is encrypted. The computing device 1602 may use any technique to determine whether the data item is encrypted. In some embodiments, in block 1924 the computing device 1602 may analyze metadata or an application hint. For example, the computing device 1602 may determine that the data item is encrypted based on object metadata such as encryption key-id, encryption algorithm, or other encryption-related features. In some embodiments, in block 1926 the computing device 1602 may determine that the data item is encrypted by analyzing randomness of the data item over varied windows and window lengths. In block 1928, the computing device 1602 checks whether the data item is encrypted. If the data item is encrypted, the method 1900 branches to block 1930, in which the computing device 1602 adds an encrypted feature to the workload feature vector 1708. After adding the encrypted feature, or if the data item is not encrypted, the method 1900 is completed. After completing the method 1900, the feature vector 1708 may be used to identify a workload grouping for the current workload as described above in connection with
Illustrative examples of the technologies disclosed herein are provided below. An embodiment of the technologies may include any one or more, and any combination of, the examples described below.
Example 1 includes a computing device for policy management, the computing device comprising a cache manager to receive a data item associated with a workload; a feature extractor to extract a workload feature vector from the data item; and a workload analyzer to (i) determine a workload grouping based on the workload feature vector and (ii) determine a cache quality of service (QoS) policy based on the workload grouping; wherein the cache manager is further to apply the cache QoS policy to the workload.
Example 2 includes the subject matter of Example 1, and wherein the workload comprises an application, a database, or a virtual machine.
Example 3 includes the subject matter of any of Examples 1 and 2, and wherein the data item comprises a file, a block, or a cache line.
Example 4 includes the subject matter of any of Examples 1-3, and wherein to determine the cache QoS policy based on the workload grouping comprises to select the cache QoS policy based on a predetermined priority level associated with the workload grouping.
Example 5 includes the subject matter of any of Examples 1-4, and wherein to determine the cache QoS policy based on the workload grouping comprises to determine the cache QoS policy with a machine learning model based on the workload feature vector.
Example 6 includes the subject matter of any of Examples 1-5, and wherein the cache QoS policy comprises a guaranteed or maximum bandwidth, a guaranteed or maximum I/O operations per second, or a maximum latency.
Example 7 includes the subject matter of any of Examples 1-6, and wherein the cache QoS policy comprises a caching mode.
Example 8 includes the subject matter of any of Examples 1-7, and wherein to apply the cache QoS policy comprises to allocate cache space associated with the workload.
Example 9 includes the subject matter of any of Examples 1-8, and wherein to receive the data item further comprises to receive an application hint associated with the data item.
Example 10 includes the subject matter of any of Examples 1-9, and wherein to extract the workload feature vector comprises to extract the application hint.
Example 11 includes the subject matter of any of Examples 1-10, and wherein to extract the workload feature vector comprises to parse data content of the data item.
Example 12 includes the subject matter of any of Examples 1-11, and wherein to extract the workload feature vector further comprises to identify a media format in response to parsing of the data content.
Example 13 includes the subject matter of any of Examples 1-12, and wherein to extract the workload feature vector further comprises to identify a database format in response to parsing of the data content.
Example 14 includes the subject matter of any of Examples 1-13, and wherein to extract the workload feature vector further comprises to identify a sensor data format in response to parsing of the data content.
Example 15 includes the subject matter of any of Examples 1-14, and wherein to extract the workload feature vector comprises to identify a volume format or a filesystem format of the data item.
Example 16 includes a method for policy management, the method comprising receiving, by a computing device, a data item associated with a workload; extracting, by the computing device, a workload feature vector from the data item; determining, by the computing device, a workload grouping based on the workload feature vector; determining, by the computing device, a cache quality of service (QoS) policy based on the workload grouping; and applying, by the computing device, the cache QoS policy to the workload.
Example 17 includes the subject matter of Example 16, and wherein the workload comprises an application, a database, or a virtual machine.
Example 18 includes the subject matter of any of Examples 16 and 17, and wherein the data item comprises a file, a block, or a cache line.
Example 19 includes the subject matter of any of Examples 16-18, and wherein determining the cache QoS policy based on the workload grouping comprises selecting the cache QoS policy based on a predetermined priority level associated with the workload grouping.
Example 20 includes the subject matter of any of Examples 16-19, and wherein determining the cache QoS policy based on the workload grouping comprises determining the cache QoS policy with a machine learning model based on the workload feature vector.
Example 21 includes the subject matter of any of Examples 16-20, and wherein applying the cache QoS policy comprises applying a guaranteed or maximum bandwidth, a guaranteed or maximum I/O operations per second, or a maximum latency.
Example 22 includes the subject matter of any of Examples 16-21, and wherein applying the cache QoS policy comprises applying a caching mode.
Example 23 includes the subject matter of any of Examples 16-22, and wherein applying the cache QoS policy comprises allocating cache space associated with the workload.
Example 24 includes the subject matter of any of Examples 16-23, and wherein receiving the data item further comprises receiving an application hint associated with the data item.
Example 25 includes the subject matter of any of Examples 16-24, and wherein extracting the workload feature vector comprises extracting the application hint.
Example 26 includes the subject matter of any of Examples 16-25, and wherein extracting the workload feature vector comprises parsing data content of the data item.
Example 27 includes the subject matter of any of Examples 16-26, and wherein extracting the workload feature vector further comprises identifying a media format in response to parsing the data content.
Example 28 includes the subject matter of any of Examples 16-27, and wherein extracting the workload feature vector further comprises identifying a database format in response to parsing the data content.
Example 29 includes the subject matter of any of Examples 16-28, and wherein extracting the workload feature vector further comprises identifying a sensor data format in response to parsing the data content.
Example 30 includes the subject matter of any of Examples 16-29, and wherein extracting the workload feature vector comprises identifying a volume format or a filesystem format of the data item.
Example 31 includes a computing device comprising a processor; and a memory having stored therein a plurality of instructions that when executed by the processor cause the computing device to perform the method of any of Examples 16-30.
Example 32 includes one or more non-transitory, computer readable storage media comprising a plurality of instructions stored thereon that in response to being executed result in a computing device performing the method of any of Examples 16-30.
Example 33 includes a computing device comprising means for performing the method of any of Examples 16-30.
