TECHNOLOGIES FOR PROVIDING EFFICIENT DISTRIBUTED DATA STORAGE IN A DISAGGREGATED ARCHITECTURE

BACKGROUND

Datacenters are evolving towards a disaggregated architecture where storage media can be shared, so as to address issues of underutilization of capacity and/or throughput of storage devices in datacenters due to imbalanced requirements across applications and over time. Remote access to storage media allows deploying storage-focused servers dedicated to serving up a large number of data storage devices. Such storage-focused servers are provisioned with relatively modest processing power that is sufficient to manage the storage media populated in the server, but nothing more, so as to avoid islands of underutilized resources (e.g., processors) within the datacenter. Simultaneously, software defined storage (SDS) distributed frameworks are being deployed within datacenters to provide high availability, reliability and durability guarantees for storage. Typically these frameworks utilize servers with robust processing power to provide the storage guarantees that they offer up to applications.

In a datacenter in which resources are disaggregated, SDS frameworks would need to be deployed such that the framework components that are compute intensive run on compute-focused servers while offering up storage media to applications from remote storage-focused servers. The problem with this approach, however, is that such a design causes a significant increase in network traffic and bandwidth utilization within the datacenter (e.g., between the compute-focused servers and the storage-focused servers). For reliability, any data written into an SDS cluster is transparently replicated on multiple nodes in the cluster. Normally this is handled by a primary server sending copies of the data to multiple replica servers that each persist a copy of the data. When storage is disaggregated, the process of each of the servers persisting the data itself requires another network hop to the eventual storage target. Erasure coding, another popular reliability mechanism also suffers from the same problem. Further, the extra network hops within the datacenter also cause an increase in the latency for access to storage.

BRIEF DESCRIPTION OF THE DRAWINGS

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.

FIG. 1 is a simplified diagram of at least one embodiment of a data center for executing workloads with disaggregated resources;

FIG. 2 is a simplified diagram of at least one embodiment of a pod that may be included in the data center of FIG. 1;

FIG. 3 is a perspective view of at least one embodiment of a rack that may be included in the pod of FIG. 2;

FIG. 4 is a side elevation view of the rack of FIG. 3;

FIG. 5 is a perspective view of the rack of FIG. 3 having a sled mounted therein;

FIG. 6 is a is a simplified block diagram of at least one embodiment of a top side of the sled of FIG. 5;

FIG. 7 is a simplified block diagram of at least one embodiment of a bottom side of the sled of FIG. 6;

FIG. 8 is a simplified block diagram of at least one embodiment of a compute sled usable in the data center of FIG. 1;

FIG. 9 is a top perspective view of at least one embodiment of the compute sled of FIG. 8;

FIG. 10 is a simplified block diagram of at least one embodiment of an accelerator sled usable in the data center of FIG. 1;

FIG. 11 is a top perspective view of at least one embodiment of the accelerator sled of FIG. 10;

FIG. 12 is a simplified block diagram of at least one embodiment of a storage sled usable in the data center of FIG. 1;

FIG. 13 is a top perspective view of at least one embodiment of the storage sled of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of a memory sled usable in the data center of FIG. 1;

FIG. 15 is a simplified block diagram of a system that may be established within the data center of FIG. 1 to execute workloads with managed nodes composed of disaggregated resources;

FIG. 16 is a simplified block diagram of at least one embodiment of a system for providing efficient data storage in a disaggregated architecture; and

FIGS. 17-20 are a simplified block diagram of at least one embodiment of a method for executing control plane management operations that may be performed by a compute sled of FIG. 16 to provide efficient data storage in a disaggregated architecture.

DETAILED DESCRIPTION OF THE DRAWINGS

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 FIG. 1, a data center 100 in which disaggregated resources may cooperatively execute one or more workloads (e.g., applications on behalf of customers) includes multiple pods 110, 120, 130, 140, each of which includes one or more rows of racks. Of course, although data center 100 is shown with multiple pods, in some embodiments, the data center 100 may be embodied as a single pod. As described in more detail herein, each rack houses multiple sleds, each of which may be primarily equipped with a particular type of resource (e.g., memory devices, data storage devices, accelerator devices, general purpose processors), i.e., resources that can be logically coupled to form a composed node, which can act as, for example, a server. In the illustrative embodiment, the sleds in each pod 110, 120, 130, 140 are connected to multiple pod switches (e.g., switches that route data communications to and from sleds within the pod). The pod switches, in turn, connect with spine switches 150 that switch communications among pods (e.g., the pods 110, 120, 130, 140) in the data center 100. In some embodiments, the sleds may be connected with a fabric using Intel Omni-Path technology. In other embodiments, the sleds may be connected with other fabrics, such as InfiniBand or Ethernet. As described in more detail herein, resources within sleds in the data center 100 may be allocated to a group (referred to herein as a “managed node”) containing resources from one or more sleds to be collectively utilized in the execution of a workload. The workload can execute as if the resources belonging to the managed node were located on the same sled. The resources in a managed node may belong to sleds belonging to different racks, and even to different pods 110, 120, 130, 140. As such, some resources of a single sled may be allocated to one managed node while other resources of the same sled are allocated to a different managed node (e.g., one processor assigned to one managed node and another processor of the same sled assigned to a different managed node).

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 FIG. 2, the pod 110, in the illustrative embodiment, includes a set of rows 200, 210, 220, 230 of racks 240. Each rack 240 may house multiple sleds (e.g., sixteen sleds) and provide power and data connections to the housed sleds, as described in more detail herein. In the illustrative embodiment, the racks in each row 200, 210, 220, 230 are connected to multiple pod switches 250, 260. The pod switch 250 includes a set of ports 252 to which the sleds of the racks of the pod 110 are connected and another set of ports 254 that connect the pod 110 to the spine switches 150 to provide connectivity to other pods in the data center 100. Similarly, the pod switch 260 includes a set of ports 262 to which the sleds of the racks of the pod 110 are connected and a set of ports 264 that connect the pod 110 to the spine switches 150. As such, the use of the pair of switches 250, 260 provides an amount of redundancy to the pod 110. For example, if either of the switches 250, 260 fails, the sleds in the pod 110 may still maintain data communication with the remainder of the data center 100 (e.g., sleds of other pods) through the other switch 250, 260. Furthermore, in the illustrative embodiment, the switches 150, 250, 260 may be embodied as dual-mode optical switches, capable of routing both Ethernet protocol communications carrying Internet Protocol (IP) packets and communications according to a second, high-performance link-layer protocol (e.g., Intel's Omni-Path Architecture's, InfiniBand, PCI Express) via optical signaling media of an optical fabric.

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 FIG. 2 (e.g., each pod may have rows of racks housing multiple sleds as described above). Additionally, while two pod switches 250, 260 are shown, it should be understood that in other embodiments, each pod 110, 120, 130, 140 may be connected to a different number of pod switches, providing even more failover capacity. Of course, in other embodiments, pods may be arranged differently than the rows-of-racks configuration shown in FIGS. 1-2. For example, a pod may be embodied as multiple sets of racks in which each set of racks is arranged radially, i.e., the racks are equidistant from a center switch.

Referring now to FIGS. 3-5, each illustrative rack 240 of the data center 100 includes two elongated support posts 302, 304, which are arranged vertically. For example, the elongated support posts 302, 304 may extend upwardly from a floor of the data center 100 when deployed. The rack 240 also includes one or more horizontal pairs 310 of elongated support arms 312 (identified in FIG. 3 via a dashed ellipse) configured to support a sled of the data center 100 as discussed below. One elongated support arm 312 of the pair of elongated support arms 312 extends outwardly from the elongated support post 302 and the other elongated support arm 312 extends outwardly from the elongated support post 304.

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 FIG. 4, a user (or robot) aligns the chassis-less circuit board substrate of an illustrative chassis-less sled 400 to a sled slot 320. The user, or robot, may then slide the chassis-less circuit board substrate forward into the sled slot 320 such that each side edge 414 of the chassis-less circuit board substrate is received in a corresponding circuit board slot 380 of the circuit board guides 330 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320 as shown in FIG. 4. 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. Furthermore, the sleds are configured to blindly mate with power and data communication cables in each rack 240, enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced. As such, in some embodiments, the data center 100 may operate (e.g., execute workloads, undergo maintenance and/or upgrades, etc.) without human involvement on the data center floor. In other embodiments, a human may facilitate one or more maintenance or upgrade operations in the data center 100.

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 FIG. 3. The illustrative rack 240 includes seven pairs 310 of elongated support arms 312 that define a corresponding seven sled slots 320, each configured to receive and support a corresponding sled 400 as discussed above. Of course, in other embodiments, the rack 240 may include additional or fewer pairs 310 of elongated support arms 312 (i.e., additional or fewer sled slots 320). It should be appreciated that because the sled 400 is chassis-less, the sled 400 may have an overall height that is different than typical servers. As such, in some embodiments, the height of each sled slot 320 may be shorter than the height of a typical server (e.g., shorter than a single rank unit, “1 U”). That is, the vertical distance between each pair 310 of elongated support arms 312 may be less than a standard rack unit “1 U.” Additionally, due to the relative decrease in height of the sled slots 320, the overall height of the rack 240 in some embodiments may be shorter than the height of traditional rack enclosures. For example, in some embodiments, each of the elongated support posts 302, 304 may have a length of six feet or less. Again, in other embodiments, the rack 240 may have different dimensions. For example, in some embodiments, the vertical distance between each pair 310 of elongated support arms 312 may be greater than a standard rack until “1 U”. In such embodiments, the increased vertical distance between the sleds allows for larger heat sinks to be attached to the physical resources and for larger fans to be used (e.g., in the fan array 370 described below) for cooling each sled, which in turn can allow the physical resources to operate at increased power levels. Further, it should be appreciated that the rack 240 does not include any walls, enclosures, or the like. Rather, the rack 240 is an enclosure-less rack that is opened to the local environment. Of course, in some cases, an end plate may be attached to one of the elongated support posts 302, 304 in those situations in which the rack 240 forms an end-of-row rack in the data center 100.

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 FIG. 6, the sled 400, in the illustrative embodiment, is configured to be mounted in a corresponding rack 240 of the data center 100 as discussed above. In some embodiments, each sled 400 may be optimized or otherwise configured for performing particular tasks, such as compute tasks, acceleration tasks, data storage tasks, etc. For example, the sled 400 may be embodied as a compute sled 800 as discussed below in regard to FIGS. 8-9, an accelerator sled 1000 as discussed below in regard to FIGS. 10-11, a storage sled 1200 as discussed below in regard to FIGS. 12-13, or as a sled optimized or otherwise configured to perform other specialized tasks, such as a memory sled 1400, discussed below in regard to FIG. 14.

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 FIG. 6, the various physical resources mounted to the chassis-less circuit board substrate 602 are mounted in corresponding locations such that no two substantively heat-producing electrical components shadow each other as discussed in more detail below. That is, no two electrical components, which produce appreciable heat during operation (i.e., greater than a nominal heat sufficient enough to adversely impact the cooling of another electrical component), are mounted to the chassis-less circuit board substrate 602 linearly in-line with each other along the direction of the airflow path 608 (i.e., along a direction extending from the front edge 610 toward the rear edge 612 of the chassis-less circuit board substrate 602).

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 FIG. 6, it should be appreciated that the sled 400 may include one, two, or more physical resources 620 in other embodiments. The physical resources 620 may be embodied as any type of processor, controller, or other compute circuit capable of performing various tasks such as compute functions and/or controlling the functions of the sled 400 depending on, for example, the type or intended functionality of the sled 400. For example, as discussed in more detail below, the physical resources 620 may be embodied as high-performance processors in embodiments in which the sled 400 is embodied as a compute sled, as accelerator co-processors or circuits in embodiments in which the sled 400 is embodied as an accelerator sled, storage controllers in embodiments in which the sled 400 is embodied as a storage sled, or a set of memory devices in embodiments in which the sled 400 is embodied as a memory sled.

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 FIG. 7) of the chassis-less circuit board substrate 602 directly opposite of the processors 820 (see FIG. 8), and power is routed from the voltage regulators to the processors 820 by vias extending through the circuit board substrate 602. Such a configuration provides an increased thermal budget, additional current and/or voltage, and better voltage control relative to typical printed circuit boards in which processor power is delivered from a voltage regulator, in part, by printed circuit traces.

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 FIG. 7, in addition to the physical resources 630 mounted on the top side 650 of the chassis-less circuit board substrate 602, the sled 400 also includes one or more memory devices 720 mounted to a bottom side 750 of the chassis-less circuit board substrate 602. That is, the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board. The physical resources 620 are communicatively coupled to the memory devices 720 via the I/O subsystem 622. For example, the physical resources 620 and the memory devices 720 may be communicatively coupled by one or more vias extending through the chassis-less circuit board substrate 602. Each physical resource 620 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments. Alternatively, in other embodiments, each physical resource 620 may be communicatively coupled to each memory device 720.

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 FIG. 8, in some embodiments, the sled 400 may be embodied as a compute sled 800. The compute sled 800 is optimized, or otherwise configured, to perform compute tasks. Of course, as discussed above, the compute sled 800 may rely on other sleds, such as acceleration sleds and/or storage sleds, to perform such compute tasks. The compute sled 800 includes various physical resources (e.g., electrical components) similar to the physical resources of the sled 400, which have been identified in FIG. 8 using the same reference numbers. The description of such components provided above in regard to FIGS. 6 and 7 applies to the corresponding components of the compute sled 800 and is not repeated herein for clarity of the description of the compute sled 800.

In the illustrative compute sled 800, the physical resources 620 are embodied as processors 820. Although only two processors 820 are shown in FIG. 8, it should be appreciated that the compute sled 800 may include additional processors 820 in other embodiments. Illustratively, the processors 820 are embodied as high-performance processors 820 and may be configured to operate at a relatively high power rating. Although the processors 820 generate additional heat operating at power ratings greater than typical processors (which operate at around 155-230 W), the enhanced thermal cooling characteristics of the chassis-less circuit board substrate 602 discussed above facilitate the higher power operation. For example, in the illustrative embodiment, the processors 820 are configured to operate at a power rating of at least 250 W. In some embodiments, the processors 820 may be configured to operate at a power rating of at least 350 W.

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 FIG. 9, an illustrative embodiment of the compute sled 800 is shown. As shown, the processors 820, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Any suitable attachment or mounting technology may be used to mount the physical resources of the compute sled 800 to the chassis-less circuit board substrate 602. For example, the various physical resources may be mounted in corresponding sockets (e.g., a processor socket), holders, or brackets. In some cases, some of the electrical components may be directly mounted to the chassis-less circuit board substrate 602 via soldering or similar techniques.

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 FIG. 9.

Referring now to FIG. 10, in some embodiments, the sled 400 may be embodied as an accelerator sled 1000. The accelerator sled 1000 is configured, to perform specialized compute tasks, such as machine learning, encryption, hashing, or other computational-intensive task. In some embodiments, for example, a compute sled 800 may offload tasks to the accelerator sled 1000 during operation. The accelerator sled 1000 includes various components similar to components of the sled 400 and/or compute sled 800, which have been identified in FIG. 10 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the accelerator sled 1000 and is not repeated herein for clarity of the description of the accelerator sled 1000.

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 FIG. 10, it should be appreciated that the accelerator sled 1000 may include additional accelerator circuits 1020 in other embodiments. For example, as shown in FIG. 11, the accelerator sled 1000 may include four accelerator circuits 1020 in some embodiments. The accelerator circuits 1020 may be embodied as any type of processor, co-processor, compute circuit, or other device capable of performing compute or processing operations. For example, the accelerator circuits 1020 may be embodied as, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), neuromorphic processor units, quantum computers, machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.

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 FIG. 11, an illustrative embodiment of the accelerator sled 1000 is shown. As discussed above, the accelerator circuits 1020, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, the individual accelerator circuits 1020 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 as discussed above. The memory devices 720 of the accelerator sled 1000 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 600. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the accelerator circuits 1020 located on the top side 650 via the I/O subsystem 622 (e.g., through vias). Further, each of the accelerator circuits 1020 may include a heatsink 1070 that is larger than a traditional heatsink used in a server. As discussed above with reference to the heatsinks 870, the heatsinks 1070 may be larger than traditional heatsinks because of the “free” area provided by the memory resources 720 being located on the bottom side 750 of the chassis-less circuit board substrate 602 rather than on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may be embodied as a storage sled 1200. The storage sled 1200 is configured, to store data in a data storage 1250 local to the storage sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may store and retrieve data from the data storage 1250 of the storage sled 1200. The storage sled 1200 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 12 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the storage sled 1200 and is not repeated herein for clarity of the description of the storage sled 1200.

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 FIG. 12, it should be appreciated that the storage sled 1200 may include additional storage controllers 1220 in other embodiments. The storage controllers 1220 may be embodied as any type of processor, controller, or control circuit capable of controlling the storage and retrieval of data into the data storage 1250 based on requests received via the communication circuit 830. In the illustrative embodiment, the storage controllers 1220 are embodied as relatively low-power processors or controllers. For example, in some embodiments, the storage controllers 1220 may be configured to operate at a power rating of about 75 watts.

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 FIG. 13, an illustrative embodiment of the storage sled 1200 is shown. In the illustrative embodiment, the data storage 1250 is embodied as, or otherwise includes, a storage cage 1252 configured to house one or more solid state drives (SSDs) 1254. To do so, the storage cage 1252 includes a number of mounting slots 1256, each of which is configured to receive a corresponding solid state drive 1254. Each of the mounting slots 1256 includes a number of drive guides 1258 that cooperate to define an access opening 1260 of the corresponding mounting slot 1256. The storage cage 1252 is secured to the chassis-less circuit board substrate 602 such that the access openings face away from (i.e., toward the front of) the chassis-less circuit board substrate 602. As such, solid state drives 1254 are accessible while the storage sled 1200 is mounted in a corresponding rack 204. For example, a solid state drive 1254 may be swapped out of a rack 240 (e.g., via a robot) while the storage sled 1200 remains mounted in the corresponding rack 240.

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 FIG. 13, the storage controllers 1220, the communication circuit 830, and the optical data connector 834 are illustratively mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, as discussed above, any suitable attachment or mounting technology may be used to mount the electrical components of the storage sled 1200 to the chassis-less circuit board substrate 602 including, for example, sockets (e.g., a processor socket), holders, brackets, soldered connections, and/or other mounting or securing techniques.

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 FIG. 14, in some embodiments, the sled 400 may be embodied as a memory sled 1400. The storage sled 1400 is optimized, or otherwise configured, to provide other sleds 400 (e.g., compute sleds 800, accelerator sleds 1000, etc.) with access to a pool of memory (e.g., in two or more sets 1430, 1432 of memory devices 720) local to the memory sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may remotely write to and/or read from one or more of the memory sets 1430, 1432 of the memory sled 1200 using a logical address space that maps to physical addresses in the memory sets 1430, 1432. The memory sled 1400 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 14 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the memory sled 1400 and is not repeated herein for clarity of the description of the memory sled 1400.

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 FIG. 14, it should be appreciated that the memory sled 1400 may include additional memory controllers 1420 in other embodiments. The memory controllers 1420 may be embodied as any type of processor, controller, or control circuit capable of controlling the writing and reading of data into the memory sets 1430, 1432 based on requests received via the communication circuit 830. In the illustrative embodiment, each memory controller 1420 is connected to a corresponding memory set 1430, 1432 to write to and read from memory devices 720 within the corresponding memory set 1430, 1432 and enforce any permissions (e.g., read, write, etc.) associated with sled 400 that has sent a request to the memory sled 1400 to perform a memory access operation (e.g., read or write).

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 FIG. 15, a system for executing one or more workloads (e.g., applications) may be implemented in accordance with the data center 100. In the illustrative embodiment, the system 1510 includes an orchestrator server 1520, which may be embodied as a managed node comprising a compute device (e.g., a processor 820 on a compute sled 800) executing management software (e.g., a cloud operating environment, such as OpenStack) that is communicatively coupled to multiple sleds 400 including a large number of compute sleds 1530 (e.g., each similar to the compute sled 800), memory sleds 1540 (e.g., each similar to the memory sled 1400), accelerator sleds 1550 (e.g., each similar to the memory sled 1000), and storage sleds 1560 (e.g., each similar to the storage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 may be grouped into a managed node 1570, such as by the orchestrator server 1520, to collectively perform a workload (e.g., an application 1532 executed in a virtual machine or in a container). The managed node 1570 may be embodied as an assembly of physical resources 620, such as processors 820, memory resources 720, accelerator circuits 1020, or data storage 1250, from the same or different sleds 400. Further, the managed node may be established, defined, or “spun up” by the orchestrator server 1520 at the time a workload is to be assigned to the managed node or at any other time, and may exist regardless of whether any workloads are presently assigned to the managed node. In the illustrative embodiment, the orchestrator server 1520 may selectively allocate and/or deallocate physical resources 620 from the sleds 400 and/or add or remove one or more sleds 400 from the managed node 1570 as a function of quality of service (QoS) targets (e.g., performance targets associated with a throughput, latency, instructions per second, etc.) associated with a service level agreement for the workload (e.g., the application 1532). In doing so, the orchestrator server 1520 may receive telemetry data indicative of performance conditions (e.g., throughput, latency, instructions per second, etc.) in each sled 400 of the managed node 1570 and compare the telemetry data to the quality of service targets to determine whether the quality of service targets are being satisfied. The orchestrator server 1520 may additionally determine whether one or more physical resources may be deallocated from the managed node 1570 while still satisfying the QoS targets, thereby freeing up those physical resources for use in another managed node (e.g., to execute a different workload). Alternatively, if the QoS targets are not presently satisfied, the orchestrator server 1520 may determine to dynamically allocate additional physical resources to assist in the execution of the workload (e.g., the application 1532) while the workload is executing. Similarly, the orchestrator server 1520 may determine to dynamically deallocate physical resources from a managed node if the orchestrator server 1520 determines that deallocating the physical resource would result in QoS targets still being met.

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 FIG. 16, a system 1610 for providing efficient data storage in a disaggregated architecture includes an orchestrator server 1620 similar to the orchestrator server 1520, in communication with multiple sleds 1616, including a compute sled 1630 that executes an application 1650 (e.g., a workload), similar to the application 1532, on behalf of a client device 1614. The system 1610 additionally includes multiple compute sleds 1632, 1634, 1636 that make up compute-focused portions of a distributed storage system (e.g., software defined storage servers, replication servers, object storage daemons, etc.) and data storage sleds 1640, 1642, 1644 that form data storage-focused portions of the distributed storage system. The compute sleds 1632, 1634, 1636 and data storage sleds 1640, 1642, 1644 may be organized into different failure domains 1690, 1692, 1694, each of which may be embodied as any physical grouping of compute devices (e.g., sleds 1616) and associated infrastructure, that may become inoperative (e.g., due to a loss of power) without affecting the availability of devices and infrastructure in other failure domains. Each compute sled 1632, 1634, 1636 operates a control plane manager 1660, which may be embodied as any circuitry or logic (e.g., a co-processor, an ASIC, software, etc.) capable of performing control plane management operations, such as placement grouping, logical storage pooling, and failure domain mapping functions, to determine which data storage sleds 1640, 1642, 1644 are to store a data set, referred to herein as an object. Further, each control plane data manager 1660, 1662, 1664, in the illustrative embodiment, executes a map data service 1661, 1663, 1665, which may be embodied as a set of operations (e.g., a thread) performed by a processor or other circuitry that provide, to a requesting compute device (e.g., a compute sled 1632, 1634, 1636), an identifier of any data storage sleds 1640, 1642, 1644 to which a particular object (e.g., identified by an object identifier) is mapped (e.g., stored on). As such, the compute sleds 1632, 1634, 1636 may share information indicative of the data storage sleds 1640, 1642, 1644 which are the targets of the SDS control components executing on the respective compute sleds 1632, 1634, 1636. Further, in response to a request (e.g., from the compute sled 1630) to store an object, a compute sled (e.g., the compute sled 1632) having a control plane data manager (e.g., the control plane data manager 1660) sends the object directly to multiple data storage sleds without also sending the object to the other compute sleds 1634, 1636 that make up the distributed data storage system. Rather, the compute sled 1632 sends only metadata to the other compute sleds 1634, 1636, with the object identifier. As such, rather than sending the object to each compute sled to then forward on to a corresponding storage sled, the compute sled that received the object from a requesting compute device (e.g., the compute sled 1630) sends the object directly to the data storage sleds 1640, 1642, 1644, thereby decreasing the number of network hops that would otherwise be required to distributively store the object and increasing the efficiency of the system 1610.

In the illustrative embodiment, data plane management operations (e.g., block allocation, fragmentation management, wear leveling, etc.), which are typically performed by the same compute device that performs the control plane management operations, are decoupled from the compute sleds 1632, 1634, 1636 and, instead, are performed by data plane managers 1670, 1672, 1674, each of which may be embodied as any circuitry or logic (e.g., a co-processor, an ASIC, software, etc. capable of performing data plane operations, on the same sled (e.g., the data storage sled(s)) where an object is actually written to and/or read from physical storage media (e.g., the data storage devices 1680, 1682, 1684, which are similar to the data storage 1250 of FIG. 12). As such, the control plane management operations, which may be compute intensive, are confined to the compute sleds 1632, 1634, 1636, which are relatively better equipped to perform compute-intensive tasks, while the data plane management operations, which pertain to management of the physical storage media, are kept local to the corresponding data storage sleds 1640, 1642, 1644 where the data storage devices 1680, 1682, 1684 are physically located.

The orchestrator server 1620, the sleds 1616, and the client device 1614 are illustratively in communication via a network 1612, which may be embodied as any type of wired or wireless communication network, including global networks (e.g., the Internet), local area networks (LANs) or wide area networks (WANs), cellular networks (e.g., Global System for Mobile Communications (GSM), 3G, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc.), digital subscriber line (DSL) networks, cable networks (e.g., coaxial networks, fiber networks, etc.), or any combination thereof.

Referring now to FIG. 17, the compute sled 1632, in operation, may execute a method 1700 for executing control plane operations to provide efficient data storage in a disaggregated architecture. The method 1700 begins with block 1702, in which the compute sled 1632 determines whether a request to locate an object has been received (e.g., from another compute sled 1634, 1636 equipped with a control plane manager 1662, 1664). In the illustrative embodiment, the request includes an object identifier, which may be any sequence of characters, numbers, or other symbols that uniquely identify the object in the distributed storage system (e.g., among the data storage sleds 1640, 1642, 1644). If the compute sled 1632 has received a request to locate an object, the method 1700 advances to block 1704, in which the compute sled 1632 determines whether the object identifier is mapped to one of more data storage sleds, as indicated in block 1704. In doing so, and as indicated in block 1706, the compute sled 1632 compares, with a map data service (e.g., the map data service 1661) of the compute sled 1632, the object identifier to a local database (e.g., in the memory 720) of object identifiers for objects stored in its target data storage sled 1640. If nothing has been stored in the distributed data storage system yet, then the compute sled 1632 will not find the object identifier from the request in the database. Otherwise, the compute sled 1632 may find a matching object identifier in the database with data indicative of an association with its data storage sled 1640 (e.g., an identifier of the data storage sled 1640, such as a Media Access Control (MAC) address). In block 1708, the compute sled 1632 determines the subsequent course of action as a function of whether the compute sled 1632 found a matching object identifier in the database (e.g., whether the object identifier is mapped to its target data storage sled 1640). If so, the method 1700 advances to block 1710, in which the compute sled 1632 returns identifiers of data storage sled to which the object identifier is mapped (e.g., the data storage sled 1640). Otherwise (e.g., if the object identifier is not found in the database), the method 1700 advances to block 1712, in which the compute sled 1632 may return a message to the requesting compute device (e.g., the compute sled 1634), that the object identifier was not found. In the illustrative embodiment, the requesting compute sled 1634 may only send such a request if it is to perform a read operation, because, for a write operation, the compute sled 1634 may simply overwrite any object having the same object identifier. In either case, the method 1700 subsequently loops back to block 1702. If, in block 1702, a request to locate an object has not been received, the method 1700 advances to block 1714 of FIG. 18, in which the compute sled 1632 receives a data access request (e.g., from the compute sled 1630 executing the application 1650). For example, the data access request may be a PUT request or a GET request from a representational state transfer (REST) application programming interface (API)) of the application 1650, as indicated in block 1716.

As indicated in block 1718, the compute sled 1632 may obtain a write request (e.g., a PUT request, as stated above). In receiving a write request, the compute sled 1632 may receive, from the requesting compute device (e.g., the compute sled 1630) a data payload (e.g., a data set) indicative of the object to be stored, as indicated in block 1720. Alternatively, the compute sled 1632 may receive a read request (e.g., a GET request, as stated above), as indicated in block 1722. The data access request may include an object identifier, which may be any sequence of characters, numbers, or other symbols that identify the object to be stored or read, as indicated in block 1724. Subsequently, in block 1726, the compute sled 1632 determines the subsequent course of action as a function of whether the data access request is a write request.

If the compute sled 1632 determines that the data access request is a write request, the method 1700 advances to block 1728, in which the compute sled 1632 may verify that the data payload represents an unstored object (e.g., an object that is not already stored in the distributed storage system). In doing so, the compute sled 1632 may produce an object identifier from a uniform resource locator (URL) of the object (e.g., by performing a hashing function on the URL), as indicated in block 1730. If the write request received in block 1714 included an object identifier, the compute sled 1632 may skip the production of an object identifier in block 1730. In block 1732, the compute sled 1632 determines whether the object identifier corresponds to a stored object. In doing so, the compute sled 1632, in the illustrative embodiment, compares the object identifier to a local database (e.g., in the memory 720) of object identifiers for objects stored in its data storage sled 1640 (e.g., the same database referenced in block 1706 of FIG. 17), as indicated in block 1734. Additionally or alternatively, as indicated in block 1736, the compute sled 1632 may query (e.g., send an object location request to) one or more other compute sleds 1634, 1636 executing map data service(s) 1663, 1665 to locate the object and receive corresponding response(s) (message(s) that the object identifier was not found, as in block 1712 of FIG. 17, or one or more identifiers of data storage sleds to which the object identifier is mapped, as in block 1710 of FIG. 17). In other embodiments, the compute sled 1632 may avoid querying the other compute sleds 1634, 1636 in block 1736 to reduce latency. In block 1738, the compute sled 1632 determines the subsequent course of action as a function of whether the payload represents an unstored object. If the data payload does not represent an unstored object (e.g., the object identifier matches the object identifier of an already-stored object), the method 1700 advances to block 1740, in which the compute sled 1632 sends a response message (e.g., to the compute sled 1630) that the object is already stored. In doing so, the compute sled 1632 may add the object identifier to the response message, as indicated in block 1742. Subsequently, the method 1700 loops back to block 1702 of FIG. 17, in which the compute sled 1632 awaits another request. Otherwise, if the object is not already stored, the method 1700 advances to block 1744 of FIG. 19, in which the compute sled 1632 maps the object to its target data storage sled (e.g., the data storage sled 1640). In some embodiments, the compute sled 1632 may skip the verification of whether the object identifier matches an already-stored object and proceed on the basis that any such object will be overwritten by the new object having the same object identifier.

Referring now to FIG. 19, in mapping the object to a set of data storage sleds, the compute sled 1632 may map the object as a function of the object identifier, as indicated in block 1746. For example, in cases in which the object identifier is a hash associated with the object (e.g., a hash of a URL of the object), the compute sled 1632 may map the object to a set of data storage sleds as a function of the hash (e.g., using a table that associates hashes, or portions of hashes, with data storage sleds), as indicated in block 1748. As indicated in block 1750, the compute sled 1632, in the illustrative embodiment, maps the object to data storage sleds in different failure domains (e.g., the data storage sleds 1640, 1642, 1644 in corresponding failure domains 1690, 1692, 1694). As indicated in block 1752, the compute sled 1632 maps the object to a set of data storage sleds that are to store corresponding replicas (e.g., copies) or erasure coded sections of the object. As indicated in block 1754, the compute sled 1632 may associate, in a local database (e.g., the database referenced in block 1706 of FIG. 17), the object identifier with its target data storage sled 1640. Additionally, in block 1754, the compute sled 1632 identifies the counterpart compute sleds 1634, 1636 that will own the object, and uses the mapping service to identify the target data storage sleds 1642, 1644 of those counterpart compute sleds 1634, 1636.

In block 1756, the compute sled 1632 sends metadata associated with the object to other compute sleds with control plane managers (e.g., the compute sleds 1634, 1636 with control plane managers 1662, 1664) associated with the mapped data storage sleds (e.g., the data storage sleds 1642, 1644), without sending the object itself (e.g., without sending the data payload). In doing so, the compute sled 1632 may send the object identifier to the compute sleds 1634, 1636, as indicated in block 1758. Further, the compute sled 1632 may send metadata indicative of one or more attributes of the object (e.g., a data type of the object, such as an image, a video, or other data type, the size of the object, and/or other attributes), as indicated in block 1762.

Though appearing after block 1756 in FIG. 19, in block 1764, the compute sled 1632, in the illustrative embodiment, concurrently (e.g., in parallel with sending the metadata to the compute sleds 1634, 1636) sends a write request to the mapped data storage sleds 1640, 1642, 1644 using a local bus protocol that is mapped onto a network protocol. As indicated in block 1766, in sending the write request, the compute sled 1632 sends the object (e.g., the data payload) and the object identifier to the mapped data storage sleds 1640, 1642, 1644. In the illustrative embodiment, in sending the write request with a local bus protocol mapped onto a network protocol, the compute sled 1632 tunnels the write request through a non-volatile memory express over fabric (NVMeOF) connection, as indicated in block 1768. For example, the compute sled 1632 may send the write request to one or more block devices that are exposed by an NVMeOF driver of the compute sled 1632 and that are associated with (e.g., represent) the mapped data storage sleds 1640, 1642, 1644, as indicated in block 1770. As such, the compute sled 1632, in the illustrative embodiment, operates as an NVMeOF initiator and each data storage sled 1640, 1642, 1644 operates as an NVMeOF target. In some embodiments, the compute sled 1632 may send the write request to one or more data storage sleds located in a different rack, pod, or data center than the compute sled 1632, as indicated in block 1772.

Referring back to block 1726 of FIG. 18, if the compute sled 1632 instead determines that the data access request is not a write request (e.g., the data access request is a read request), the method 1700 advances to block 1774 of FIG. 20, in which the compute sled 1632 determines a set of data storage sleds that are mapped to the object identifier included in the data access request. In doing so, the compute sled 1632 may compare the object identifier to a local database of object identifiers for objects stored in one or more of the data storage sleds 1640, 1642, 1644 (e.g., the database referenced in block 1706 of FIG. 17), as indicated in block 1776. Additionally or alternatively, in block 1778, the compute sled 1632 may query (e.g., send an object location request to) one or more other compute sleds 1634, 1636 executing map data service(s) 1663, 1665 to locate the object and receive corresponding response(s) (e.g., one or more identifiers of data storage sleds to which the object identifier is mapped) from those compute sleds 1634, 1636. Subsequently, the method 1700 advances to block 1780 in which the compute sled 1632 sends a read request to one or more of the mapped data storage sleds 1640, 1642, 1644. In doing so, the compute sled 1632 sends the object identifier to one or more of the mapped data storage sleds 1640, 1642, 1644, as indicated in block 1782. In the illustrative embodiment, in sending the read request, the compute sled 1632 tunnels the read request through NVMeOF to one or more of the mapped data storage sleds 1640, 1642, 1644, as indicated in block 1784. For example, and as indicated in block 1786, the compute sled 1632 may send the read request to one or more block devices exposed by an NVMeOF driver of the compute sled 1632 and that are associated with the mapped data storage sleds 1640, 1642, 1644. As indicated in block 1788, the compute sled 1632 may send the read request to one or more data storage sleds located in a different rack, pod, or data center than the compute sled 1632. Subsequently, in block 1790, the compute sled 1632 receives the read object from one or more of the data storage sleds 1640, 1642, 1644. In doing so, in the illustrative embodiment, the compute sled 1632 receives the object through an NVMeOF connection between the data storage sled (e.g., the data storage sled 1640) and the compute sled 1632, as indicated in block 1792. In some embodiments, the compute sled 1632 may receive erasure coded sections of the object from the data storage sleds 1640, 1642, 1644 to be combined to produce the object, as indicated in block 1794. Subsequently, the method 1700 advances to block 1796, in which the compute sled 1632 sends the object to the requesting compute device. For example, and as indicated in block 1798, the compute sled 1632 sends the object to the compute sled 1630 executing the application 1650.

EXAMPLES

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 compute sled comprising a network interface controller; and circuitry to receive, through a network and with the network interface controller, a data access request from a compute device, wherein the data access request includes a data payload indicative of an object to be stored; map the object to a set of multiple data storage sleds for distributed storage of the object; and send a write request with the object and an object identifier to the mapped data storage sleds to store the object in one or more data storage devices located on each data storage sled and concurrently send metadata without the object to one or more other compute sleds associated with the mapped data storage sleds.

Example 2 includes the subject matter of Example 1, and wherein to map the object to the set of data storage sleds comprises to read, from a database present on the compute sled and associated with a map data service, data indicative of an association between an identifier of the object and the set of data storage sleds.

Example 3 includes the subject matter of any of Examples 1 and 2, and wherein the compute sled is a first compute sled and wherein the circuitry is further to receive a request from a second compute sled to determine whether a second object is mapped to a data storage sled associated with the first compute sled, wherein the request includes a second object identifier; and compare, in response to receipt of the request, the object identifier to a database present on the first compute sled, wherein the database includes identifiers of objects stored in the data storage sled associated with the first compute sled.

Example 4 includes the subject matter of any of Examples 1-3, and wherein the circuitry is further to send, in response to a determination that the second object identifier matches an identifier of an object mapped to the data storage sled, an identifier of the data storage sled associated with the second object identifier.

Example 5 includes the subject matter of any of Examples 1-4, and wherein to send, through the network and with a local data bus protocol mapped onto a network protocol, a write request comprises to tunnel the write request through a non-volatile memory express over fabric (NVMeOF) connection to the data storage sleds.

Example 6 includes the subject matter of any of Examples 1-5, and wherein to map the object to the data storage sleds comprises to map the object to data storage sleds in different failure domains.

Example 7 includes the subject matter of any of Examples 1-6, and wherein to send the metadata comprises to send one or more of an object identifier associated with the object or metadata indicative of one or more attributes of the object.

Example 8 includes the subject matter of any of Examples 1-7, and wherein to map the object to the data storage sleds comprises to map the object as a function of a hash of a uniform resource locator of the object.

Example 9 includes the subject matter of any of Examples 1-8, and wherein the circuitry is further to verify, prior to sending the write request to the mapped data storage sled, that the data payload represents an object that is not already stored.

Example 10 includes the subject matter of any of Examples 1-9, and wherein to verify that the data payload represents an object that is not already stored comprises to compare an object identifier associated with the object to a database of object identifiers for objects stored in a data storage sled associated with the compute sled.

Example 11 includes the subject matter of any of Examples 1-10, and wherein the circuitry is further to receive a request to read a second object, wherein the request includes an identifier of the second object; and determine, in response to the read request, one or more storage sleds mapped to the identifier of the second object; send, through the network and with the local data bus protocol mapped onto the network protocol, a read request to the mapped one or more data storage sleds; and receive the read object from the mapped one or more data storage sleds.

Example 12 includes the subject matter of any of Examples 1-11, and wherein to send, through the network and with the local data bus protocol mapped onto the network protocol, a read request comprises to tunnel the read request through a non-volatile memory express over fabric (NVMeOF) connection to the one or more data storage sleds.

Example 13 includes one or more machine-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a compute sled to receive, through a network and with a network interface controller, a data access request from a compute device, wherein the data access request includes a data payload indicative of an object to be stored; map the object to a set of multiple data storage sleds for distributed storage of the object; and send a write request with the object and an object identifier to the mapped data storage sleds to store the object in one or more data storage devices located on each data storage sled and concurrently send metadata without the object to one or more other compute sleds associated with the mapped data storage sleds.

Example 14 includes the subject matter of Example 13, and wherein to map the object to the set of data storage sleds comprises to read, from a database present on the compute sled and associated with a map data service, data indicative of an association between an identifier of the object and the set of data storage sleds.

Example 15 includes the subject matter of any of Examples 13 and 14, and wherein, the compute sled is a first compute sled and when executed, the plurality of instructions further cause the compute sled to receive a request from a second compute sled to determine whether a second object is mapped to a data storage sled associated with the first compute sled, wherein the request includes a second object identifier; and compare, in response to receipt of the request, the object identifier to a database present on the first compute sled, wherein the database includes identifiers of objects stored in the data storage sled associated with the first compute sled.

Example 16 includes the subject matter of any of Examples 13-15, and wherein when executed, the plurality of instructions further cause the compute sled to send, in response to a determination that the second object identifier matches an identifier of an object mapped to the data storage sled, an identifier of the data storage sled associated with the second object identifier.

Example 17 includes the subject matter of any of Examples 13-16, and wherein to send, through the network and with a local data bus protocol mapped onto a network protocol, a write request comprises to tunnel the write request through a non-volatile memory express over fabric (NVMeOF) connection to the data storage sleds.

Example 18 includes the subject matter of any of Examples 13-17, and wherein to map the object to the data storage sleds comprises to map the object to data storage sleds in different failure domains.

Example 19 includes the subject matter of any of Examples 13-18, and wherein to send the metadata comprises to send one or more of an object identifier associated with the object or metadata indicative of one or more attributes of the object.

Example 20 includes the subject matter of any of Examples 13-19, and wherein to map the object to the data storage sleds comprises to map the object as a function of a hash of a uniform resource locator of the object.

Example 21 includes the subject matter of any of Examples 13-20, and wherein the circuitry is further to verify, prior to sending the write request to the mapped data storage sled, that the data payload represents an object that is not already stored.

Example 22 includes a method comprising receiving, by a compute sled with a network interface controller, a data access request from a compute device, wherein the data access request includes a data payload indicative of an object to be stored; mapping, by the compute sled, the object to a set of multiple data storage sleds for distributed storage of the object; and sending, by the compute sled, a write request with the object and an object identifier to the mapped data storage sleds to store the object in one or more data storage devices located on each data storage sled and concurrently sending metadata without the object to one or more other compute sleds associated with the mapped data storage sleds.

Example 23 includes the subject matter of Example 22, and wherein mapping the object to the set of data storage sleds comprises reading, from a database present on the compute sled and associated with a map data service, data indicative of an association between an identifier of the object and the set of data storage sleds.

Example 24 includes the subject matter of any of Examples 22 and 23, and wherein the compute sled is a first compute sled, the method further comprising receiving, by the first compute sled, a request from a second compute sled to determine whether a second object is mapped to a data storage sled associated with the first compute sled, wherein the request includes a second object identifier; and comparing, by the first compute sled and in response to receipt of the request, the object identifier to a database present on the first compute sled, wherein the database includes identifiers of objects stored in the data storage sled associated with the first compute sled.

Example 25 includes the subject matter of any of Examples 22-24, and further including sending, by the compute sled and in response to a determination that the second object identifier matches an identifier of an object mapped to the data storage sled, an identifier of the data storage sled associated with the second object identifier.

TECHNOLOGIES FOR PROVIDING EFFICIENT DISTRIBUTED DATA STORAGE IN A DISAGGREGATED ARCHITECTURE

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