Patent Publication Number: US-2022214912-A1

Title: Sharing and oversubscription of general-purpose graphical processing units in data centers

Description:
TECHNICAL FIELD 
     Embodiments of the invention relate to the field of general-purpose graphics processing units (GPGPUs) in data centers; and more specifically, to the sharing and oversubscription of GPGPUs in data centers. 
     BACKGROUND ART 
     Hardware accelerators, such as graphical processing units (GPUs) and field-programmable gate arrays (FPGAs), can be used to accelerate data processing that are typically performed on general-purpose processors. Once GPUs were proven to be useful in speeding up specialized tasks, such as video rendering, a new generation of GPUs, referred to as general-purpose GPUs (GPGPUs), were introduced into the market. In addition to accelerating video rendering, these new GPGPUs were also designed to accelerate other heavy computational workloads, such as scientific algorithms related to big data analysis. 
     Considering that hardware accelerators might be designed to only accelerate specific tasks very efficiently, hardware accelerators might not be required for all workloads typically running in a data center. Instead, the idea of hardware disaggregation is preferred, as it aims to make hardware accelerators accessible to applications of a data center through remote access only when needed. For GPGPUs, technologies such as remote CUDA (rCUDA) allow GPGPUs to be remotely accessible from any consumer of a data center. 
     Typically, GPGPU resources are statically allocated to specific workloads, until the termination of the workload. Cloud orchestration technologies, such as Openstack® and Kubernetes®, do not allow sharing and over-provisioning of GPGPUs in data centers. Unlike general-purpose processors and associated random access memory, GPGPUs are not simultaneously shared amongst workloads. Instead, an entire GPGPU needs to be allocated to a specific workload and a new workload is not introduced until the previous workload is terminated/completed. However, not all workloads need an entire GPGPU nor will utilize a GPGPU for the entirety of an allocated time period. For example, machine learning workloads, which are mainly prediction workloads, do not require a GPGPU all the time, as opposed to GPGPU intensive training workloads. Instead, machine learning workloads require a GPGPU only when an input comes for prediction. During the remaining time period, the GPGPU is not utilized or is underutilized. Also, assuming that such workloads are not strictly time-bound or latency sensitive, seizing an entire GPGPU for such tasks could be considered inefficient. Accordingly, allocating a dedicated GPGPU for such workloads that intermittently utilize resources can lead to underutilization of GPGPUs. 
     SUMMARY 
     A method for managing general-purpose graphical processing units (GPGPUs) in a data center system is described. The method includes receiving, by a proxy agent, a first GPGPU request from a first application, wherein the first GPGPU request requests the scheduling of a first workload of the first application to a GPGPU in a set of GPGPUs of the data center system; selecting, by the proxy agent, a first GPGPU from the set of GPGPUs for processing the first workload of the first application based on one or more of (1) available resources of the set of GPGPUs and (2) requirements of the workload as indicated by the first GPGPU request; establishing, by the proxy agent, (1) a first session between an application agent located on a compute node on which the application is located and the proxy agent and (2) a second session between the first GPGPU and the proxy agent in response to selecting the first GPGPU for the first workload to allow the first GPGPU to process the first workload of the first application, including subsequent GPGPU requests associated with the first workload, wherein the first session and the second session are associated with the first workload of the first application; and collecting, by the proxy agent, a performance profile of the first workload on the first GPGPU to describe usage of resources of the first GPGPU by the first workload while the first GPGPU is processing the first workload. 
     A non-transitory machine-readable storage medium is described that provides instructions that, if executed by a processor of a proxy agent in a data center system, will cause said processor to perform operations. The operations include receiving a first GPGPU request from a first application, wherein the first GPGPU request requests the scheduling of a first workload of the first application to a GPGPU in a set of GPGPUs of the data center system; selecting a first GPGPU from the set of GPGPUs for processing the first workload of the first application based on one or more of (1) available resources of the set of GPGPUs and (2) requirements of the workload as indicated by the first GPGPU request; establishing (1) a first session between an application agent located on a compute node on which the application is located and the proxy agent and (2) a second session between the first GPGPU and the proxy agent in response to selecting the first GPGPU for the first workload to allow the first GPGPU to process the first workload of the first application, including subsequent GPGPU requests associated with the first workload, wherein the first session and the second session are associated with the first workload of the first application; and collecting a performance profile of the first workload on the first GPGPU to describe usage of resources of the first GPGPU by the first workload while the first GPGPU is processing the first workload. 
     A device for managing general-purpose graphical processing units (GPGPUs) in a data center system is described. The device is to receive a first GPGPU request from a first application, wherein the first GPGPU request requests the scheduling of a first workload of the first application to a GPGPU in a set of GPGPUs of the data center system; select a first GPGPU from the set of GPGPUs for processing the first workload of the first application based on one or more of (1) available resources of the set of GPGPUs and (2) requirements of the workload as indicated by the first GPGPU request; establish (1) a first session between an application agent located on a compute node on which the application is located and the proxy agent and (2) a second session between the first GPGPU and the proxy agent in response to selecting the first GPGPU for the first workload to allow the first GPGPU to process the first workload of the first application, including subsequent GPGPU requests associated with the first workload, wherein the first session and the second session are associated with the first workload of the first application; and collect a performance profile of the first workload on the first GPGPU to describe usage of resources of the first GPGPU by the first workload while the first GPGPU is processing the first workload. 
     As described above and as will be described below, the data center system assists in sharing resources of GPGPUs more efficiently in cloud environments by allowing GPGPUs to be oversubscribed for certain workloads/applications. In particular, workloads/applications allocated to GPGPUs are monitored to build usage/performance profiles per workload/application. These usage/performance profiles built for each workload/application can be used for predicting or otherwise better determining when a workload/application is underutilizing resources of GPGPUs. When a usage/performance profile indicates that a workload/application is underutilizing resources of a GPGPU or will likely underutilize resources of GPGPUs in the near future, the proxy agent may mark the workload/application for eviction from the current GPGPU. When the workload/application is to be processed again (e.g., a GPGPU request/command is received), the workload/application can be dynamically reassigned to another GPGPU. This dynamic movement of workloads/applications between GPGPUs removes the tight, static coupling of workloads/applications to GPGPUs. Further, through the use of emulated/virtual GPGPUs on compute nodes, GPGPU sharing is transparent to applications while still facilitating GPGPU time-sharing to reduce underutilization of GPGPUs. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention may best be understood by referring to the following description and accompanying drawings that are used to illustrate embodiments of the invention. In the drawings: 
         FIG. 1  illustrates a data center system where general-purpose graphical processing units (GPGPUs) are pooled for access by a number of applications, according to some example embodiments. 
         FIG. 2  shows a mapping table that maps identifiers of workloads/applications to identifiers of assigned GPGPUs and corresponding performance profiles, according to some example embodiments. 
         FIG. 3  illustrates a data center system with clusters/pools of GPGPUs and corresponding proxy agents, according to some example embodiments. 
         FIG. 4  shows the mapping table that also includes status information, according to some example embodiments. 
         FIGS. 5A-5C  illustrate a method for managing placement/scheduling of workloads of applications to GPGPUs in the data center system, according to one example embodiment. 
         FIG. 6  shows a first session/connection between a proxy agent and a first GPGPU agent for a workload/application, according to one example embodiment. 
         FIG. 7  shows a second session/connection between the proxy agent and a second GPGPU agent for the workload/application, according to one example embodiment. 
         FIGS. 8A-8C  illustrate a method for managing placement/scheduling of workloads of applications to GPGPUs in the data center system, according to one example embodiment. 
         FIG. 9A  illustrates connectivity between network devices (NDs) within an exemplary network, as well as three exemplary implementations of the NDs, according to some embodiments of the invention. 
         FIG. 9B  illustrates an exemplary way to implement a special-purpose network device according to some embodiments of the invention. 
         FIG. 9C  illustrates various exemplary ways in which virtual network elements (VNEs) may be coupled according to some embodiments of the invention. 
         FIG. 9D  illustrates a network with a single network element (NE) on each of the NDs, and within this straight forward approach contrasts a traditional distributed approach (commonly used by traditional routers) with a centralized approach for maintaining reachability and forwarding information (also called network control), according to some embodiments of the invention. 
         FIG. 9E  illustrates the simple case of where each of the NDs implements a single NE, but a centralized control plane has abstracted multiple of the NEs in different NDs into (to represent) a single NE in one of the virtual network(s), according to some embodiments of the invention. 
         FIG. 9F  illustrates a case where multiple VNEs are implemented on different NDs and are coupled to each other, and where a centralized control plane has abstracted these multiple VNEs such that they appear as a single VNE within one of the virtual networks, according to some embodiments of the invention. 
         FIG. 10  illustrates a general-purpose control plane device with centralized control plane (CCP) software, according to some embodiments of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     The following description describes methods and apparatus for the sharing and oversubscription of general-purpose graphics processing units (GPGPUs) in data centers. In the following description, numerous specific details such as logic implementations, opcodes, means to specify operands, resource partitioning/sharing/duplication implementations, types and interrelationships of system components, and logic partitioning/integration choices are set forth in order to provide a more thorough understanding of the present invention. It will be appreciated, however, by one skilled in the art that the invention may be practiced without such specific details. In other instances, control structures, gate level circuits and full software instruction sequences have not been shown in detail in order not to obscure the invention. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation. 
     References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the 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 affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Bracketed text and blocks with dashed borders (e.g., large dashes, small dashes, dot-dash, and dots) may be used herein to illustrate optional operations that add additional features to embodiments of the invention. However, such notation should not be taken to mean that these are the only options or optional operations, and/or that blocks with solid borders are not optional in certain embodiments of the invention. 
     In the following description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. “Coupled” is used to indicate that two or more elements, which may or may not be in direct physical or electrical contact with each other, co-operate or interact with each other. “Connected” is used to indicate the establishment of communication between two or more elements that are coupled with each other. 
       FIG. 1  illustrates a data center system  100 , according to one example embodiment, where GPGPUs  102  are pooled for access by a number of applications  104 . As used herein, each application  104  is associated with a workload to be processed/performed by a GPGPU  102 . As shown in  FIG. 1 , the data center system  100  includes a cloud orchestrator  106 , which is an entity that assists in scheduling/assigning workloads/applications  104  to GPGPUs  102  located on/within corresponding GPGPU nodes  110  (sometimes referred to as GPGPU sleds  110 ) to facilitate oversubscription/overscheduling of the GPGPUs  102 . Namely, the cloud orchestrator  106  may be used for configuring and/or managing one or more entities of the data center system  100  such that the data center system  100  can provide dynamic and efficient scheduling of workloads/applications  104  to GPGPUs  102  based on up-to-date usage/performance workload/application profiles, which are derived by monitoring workload/application  104  processing by the GPGPUs  102 . 
     Each element of the data center system  100  will be described below by way of example. Although the elements of the data center system  100  are shown in a single logical view/structure, each of the elements of the data center system  100  may be distributed across one or more devices and/or locations. 
     As shown in  FIG. 1 , the data center system  100  includes a set of GPGPU nodes  110 A- 110 Z and each GPGPU node  110 A- 110 Z includes a corresponding set of GPGPUs  102  (e.g., the GPGPUs  102 A 1 - 102 A 3  of the GPGPU node  110 A and the GPGPUs  102 Z 1  and  102 Z 2  of the GPGPU node  110 Z) with corresponding GPGPU memory  112  for each GPGPU  102  (e.g., the GPGPU memories  112 A 1 - 112 A 3  are associated with the GPGPUs  102 A 1 - 102 A 3  and the GPGPU memories  112 Z 1  and  112 Z 2  are associated with the GPGPUs  102 Z 1  and  102 Z 2 , respectively). The GPGPU memories  112  may be used by corresponding GPGPUs  102  for processing assigned workloads from corresponding applications  104 . Accordingly, the GPGPU memories  112  act as local memory to respective GPGPUs  102 . The number of GPGPU nodes  110  and corresponding GPGPUs  102  within each GPGPU node  110  may vary for different data center systems  100 . Accordingly, the configuration of  FIG. 1  is for purposes of illustration. In some embodiments, the GPGPUs  102  may vary in architecture such that separate GPGPUs  102  share or have different architectures, including potentially different amounts of GPGPU memory  112 . 
     As used herein, a graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. A GPGPU  102  is a GPU, which is designed for computation of computer graphics, that is used to perform computation in applications traditionally handled by general-purpose processors (sometimes referred to as central processing units (CPUs)). Although the data center system  100  is described as using GPGPUs  102 , in other embodiments, other hardware accelerators may be used in place of GPGPUs. For example, in some embodiments, field-programmable gate arrays (FPGAs) may be used for processing workloads of applications  104  instead of GPGPUs  102 . Accordingly, the use of GPGPUs is for purposes of illustration. 
     Each of the applications  104  discussed above may be running on or may otherwise primarily reside on a compute node  108  and the compute nodes  108  may emulate virtual GPGPUs for the applications  104  such that access to the remote GPGPUs  102  is transparent to the applications  104 . For instance, as shown in  FIG. 1 , three compute nodes  108 A- 108 C (sometimes referred to as the compute sleds  108 A- 108 C) include corresponding (1) sets of applications  104 A- 104 B,  104 C- 104 D, and  104 E- 104 F and (2) application agents  120 A- 120 C. The application agents  120 A- 120 C (sometimes referred to as the application clients  120 A- 120 C) provide an environment and set of libraries to assist applications  104  to access GPGPUs  102  through an abstraction layer provided by emulated/virtual GPGPUs. For example, in the case of a Kubernetes® system, an application agent  120  could load a customized device plugin to provide Compute Unified Device Architecture (CUDA) libraries for applications  104  to access GPGPUs  102  in the data center system  100 . 
     As shown in  FIG. 1 , the compute nodes  108  and corresponding components (e.g., applications  104  and application agents  120 ) are communicatively coupled to the data center system  100  via a proxy agent  122  of the data center system  100 . Although the compute nodes  108  are shown outside the data center system  100 , in some embodiments, the compute nodes  108  may be within the data center system  100 . However, regardless of their location, the compute nodes  108  are communicatively coupled to the proxy agent  122 . The proxy agent  122  may be used for (1) scheduling/assigning applications  104  and associated workloads to GPGPUs  102  via corresponding GPGPU agents  124  of GPGPU nodes  110 , which monitor/manage the GPGPUs  102 , (2) evicting workloads/applications  104  from GPGPUs  102  based on monitored performance information/profiles of the workloads/applications  104 , and (3) rescheduling/reassigning evicted workloads/applications  104  to other GPGPUs  102  via corresponding GPGPU agents  124  that monitor/manage these other GPGPUs  102  (e.g., the GPGPU agent  124 A monitors the GPGPUs  102 A 1 - 102 A 3  and associated GPGPU memories  112 A 1 - 112 A 3 , while the GPGPU agent  124 Z monitors the GPGPUs  102 Z 1  and  102 Z 2  and associated GPGPU memories  112 Z 1  and  112 Z 2 ). To support these functions, application agents  120  establish a dedicated session  138   1 - 138   6  with the proxy agent  122  for each associated application  104 . These sessions  138  may be established in response to receipt of GPGPU requests from associated applications  104  that describe details of a workload of the application  104  to be assigned to a GPGPU  102  (e.g., an amount of GPGPU memory  112  and/or a desired/requested GPGPU  102  architecture), which may also be defined in a service level agreement (SLA) between an operator/administrator of the data center system  100  and operators/tenants associated with the applications  104 . Alternatively, these sessions  138  may be established upon instantiation of the applications  104  on the compute nodes  108 . 
     Based on the availability of resources of the GPGPUs  102  in the various GPGPU nodes  110  and requirements of the applications  104  (e.g., as indicated in GPGPU requests from the applications  104 ), the proxy agent  122  assigns applications  104  to various GPGPUs  102 . To effectuate this assignment, the proxy agent  122  establishes a session with the corresponding GPGPU agent  124  managing/monitoring the assigned GPGPU  102  and provides the GPGPU agent  124  (1) an identifier of the application  104 , (2) an identifier of the assigned GPGPU  102 , and (3) a request to the GPGPU node  110  to execute future GPGPU requests from the application  104  by the assigned GPGPU  102 . The proxy agent  122  may also maintain a mapping between the application  104  and the GPGPU  102  for future reference. For example,  FIG. 2  shows a mapping table  200  (sometimes referred to as a workload mapping table  200  or an application mapping table  200 ) that maps identifiers  202  of workloads/applications  104  to identifiers  204  of assigned GPGPUs  102 . In some embodiments, communications between (1) the application agent  120  and the proxy agent  122  and (2) the proxy agent  122  and the GPGPU agent  124  can use a customized protocol (e.g., rCUDA) for exchanging requests and responses (e.g., successful or unsuccessful execution/processing of a request). 
     Although shown with a single proxy agent  122  for the entire data center system  100  of  FIG. 1 , in some embodiments multiple proxy agents  122  may be utilized by the data center system  100 . For example, in a data center system  100  with large pools of GPGPUs  102 , the GPGPUs  102  and corresponding GPGPU nodes  110  can be organized into clusters with each cluster including a separate proxy agent  122  to load balance the traffic between GPGPU nodes  110  of the cluster. For example, as shown in  FIG. 3 , a set of three GPGPU clusters  302 A- 302 C (sometimes referred to as the GPGPU pools  302 A- 302 C) each include a respective set of GPGPU nodes  110 A- 110 F,  110 G- 110 L, and  110 M- 110 R. In this example, each GPGPU cluster  302 A- 302 C includes a respective proxy agent  122 A- 122 C to handle commands/requests from sets of compute nodes  108  and associated applications  104  as communicated via the sessions/connections  138   1 - 138   6 . Accordingly, the data center system  100  may include one or more proxy agents  122 . 
     As shown in  FIG. 1 , each GPGPU agent  124 A- 124 Z may include a respective monitoring agent  126 A- 126 Z that monitors and profiles all the GPGPUs  102  in a corresponding GPGPU node  110 A- 110 Z, including associated resources and workloads/applications  104  being processed by the GPGPUs  102 . For example, the monitoring agents  126  can monitor active/running process kernels on GPGPUs  102 , memory utilization of each process on the GPGPUs  102 , GPGPU  102  utilization, GPGPU  102  temperature, etc. The monitoring agents  126  continuously generate monitoring information within an associated GPGPU node  110  and report this information to the proxy agent  122 . The monitoring information produced by the monitoring agents  126  can be used to form performance/usage profiles for workloads/applications  104  that describe the performance/operation of workloads of the applications  104  on GPGPUs  102  and respective GPGPU memories  112 . For example,  FIG. 2  shows the performance profile identifiers  206  of each combination of application  104  and GPGPU  102  (e.g., the application  104 A and corresponding workload that is assigned to the GPGPU  102 A 3  has a performance profile with the performance profile identifier  206  of PROFILE A,A3 ; the application  104 B and corresponding workload that is assigned to the GPGPU  102 Z 1  has a performance profile with the performance profile identifier  206  of PROFILE B,Z1 ; and the application  104 C and corresponding workload that is assigned to the GPGPU  102 A 1  has a performance profile with the performance profile identifier  206  of PROFILE C,A1 ). In some embodiments, the proxy agent  122  may use workload/application performance/usage profiles for determining candidate applications  104  for possible eviction and consequent movement to other GPGPUs  102  in potentially other GPGPU nodes  110 . Namely, as will be described in greater detail below, based on workload/application performance/usage profiles, the proxy agent  122  may determine workloads/applications  104  that are underutilizing resources of a GPGPU  102  (e.g., are idle for a period of time that is greater than a threshold idle period) and add these workloads/applications  104  to a candidate list of workloads/applications  104  for eviction. 
     In some embodiments, the GPGPUs  102 , including associated memory management units (MMUs)  116 A- 116 Z, do not maintain a reference bit or other explicit information for indicating usage of pages in page tables of the GPGPU memory  112 A- 112 Z. Accordingly, determining exact GPGPU memory  112  usage per application  104  for corresponding performance/usage profiles is challenging. To address this challenge, the monitoring agents  126  track GPGPU memory  112  allocations and corresponding virtual addresses in use for workloads of applications  104 . The monitoring agent  126  further tracks scheduling and execution of GPGPU kernels (i.e., block functions that can be scheduled and executed in parallel on multiple GPGPU simultaneously) for workloads of applications  104 . To measure the idle time of workloads of applications  104  on GPGPUs  102 , the monitoring agents  126  maintains a cumulative record of how frequently the workloads&#39; kernels are executed in the GPGPUs  102  (e.g., how frequently GPGPU requests/commands, such as cudaMalloc( ), cudaMemcpy( ), and/or cudaLanchKemel( ), are executed over the given period of time). The monitoring agent  126  may further track network activity (e.g., number of packets per seconds) for workloads of applications  104  to measure workload idle time on a GPGPU  102 . 
     If a performance/usage profile for an application  104  reveals an idle time for an associated workload on a GPGPU  102  that is greater that a given threshold usage/idle value, which may be configurable by the cloud orchestrator  106 , then the proxy agent  122  may select this workload and corresponding application  104  for eviction from the GPGPU  102 . For example, the proxy agent  122  may place this workload/application  104  on a list of workload/application candidates for eviction. Based on GPGPU  102  demand in the data center system  100  by a new or an already evicted workload/application  104  that needs to be scheduled/rescheduled to a GPGPU  102 , the proxy agent  122  may select a candidate workload/application  102  from a list of workload/application candidates with similar characteristics (e.g., a similar memory profile) to the workload/application  104  to be placed, a lower priority level, and/or based on a round robin approach. In some embodiments, the proxy agent  122  may request a corresponding GPGPU agent  124  associated with the soon to be evicted workload/application  104  to prioritize and finish any pending GPGPU requests and commands in a command queue of the GPGPU  102  to facilitate eviction with minimal impact to the evicted workload/application  104 . The GPGPU agent  124  waits for the existing requests/commands and the workload&#39;s kernel to run for its completion. Upon completion, the GPGPU agent  124  informs the proxy agent  122  that the contents of the GPGPU memory  112  associated with the evicted workload/application  104  are ready to be moved. Further, the total GPGPU memory  112  currently allocated to the workload/application  104  and the total GPGPU memory  112  available to the GPGPU  102  is also reported to the proxy agent  122 . 
     To facilitate the eviction, the proxy agent  122  contacts the remote memory management unit  128 , which manages a set of remote memory nodes  130 A- 130 M (sometimes referred to as the remote/global memory sleds  130 A- 130 M) and requests a range of memory addresses corresponding to the remote memory units  132 A 1 - 132 A N  and  132 M 1 - 132 M P  (sometimes referred to as the global memory units  132 A 1 - 132 A N  and  132 M 1 - 132 M P ) that may be used to store and retrieve the entire evicted workload&#39;s/application&#39;s  104  contents from the GPGPU  102 /GPGPU memory  112 . Unlike traditional commodity server architectures, where memory is very tightly coupled to the processing unit on the same sled/node, the remote memory management unit  128 , the remote memory nodes  130 A- 130 M, and the remote memory units  132 A 1 - 132 A N  and  132 M 1 - 132 M P  provide disaggregated hardware that offer the capability to have a portion of memory (apart from the GPGPU memory  112  that is local to the GPGPUs  102  and connected to respective GPGPUs  102  via a fast interconnect) reside in another sled/node  130 . The cloud orchestrator  106  may configure these remote memories such that the remote memory units  132 A 1 - 132 A N  and  132 M 1 - 132 M P  are accessible to GPGPUs  102  via a high-speed interconnect network of the data center system  100 . In this capacity, the remote memory units  132 A 1 - 132 A N  and  132 M 1 - 132 M P , as managed by the remote memory management unit  128 , offer a global source of memory for components of the data center system  100 , including the GPGPUs  102 . 
     For example, the remote memory management unit  128  may allocate a requested number of addresses/memory from the remote memory units  132  of the remote memory nodes  130  and returns the range of addresses/set of the remote memory units  132  to the proxy agent  122 . The remote memory management unit  128  also allocates space for a control page  134  to map virtual addresses of the evicted workload/application  104  to the corresponding addresses (e.g., physical addresses) of the remote memory units  132 . The proxy agent  122  provides the memory address range (and IP memory address, in case of RDMA) and address of the control page  134  to the GPGPU agent  124  to initiate the eviction process. 
     Following receipt of the memory address range corresponding to the remote memory units  132 , the GPGPU agent  124  provides workload&#39;s/application&#39;s  104  range of virtual addresses (i.e., the source addresses) and the memory address range in the remote memory units  132  (i.e., the destination addresses) to a direct memory access (DMA) feeder  114  to initiate the transfer of data to the remote memory units  132 . The DMA feeder(s)  114 A- 114 Z write the source and destination address into registers of the corresponding DMA unit(s)  118 A-Z (multiple addresses in parallel for all the available DMA channels). During the transfer of data from the GPGPU memory  112  to the remote memory units  132 , the DMA feeder  114  updates the allocated control page  134  with virtual addresses and corresponding addresses of the remote memory units  132 . 
     To avoid discrepancies during the data transfer, the proxy agent  122  updates a corresponding status entry for the application-to-GPGPU map to note that the application is in progress of being evicted. For example, as shown in  FIG. 4 , the table  200  of  FIG. 2  can be expanded to include a status  208 . As shown, the status  208  can indicate that a workload/application  104  is in the process of being evicted (“EVICTION”), has been evicted (“EVICTED”), or is scheduled with a GPGPU  102  (“SCHEDULED”). This notation will act to stop the GPGPU  102  from further executing commands/requests on behalf of the workload/application  104 . Further, to maintain complete transparency of the eviction to the application  104  on the compute node  108 , GPGPU requests from the application  104  received at commencement of the eviction and data transfer are buffered in a virtual queue  136  of the proxy agent  122  without forwarding them to the previously allocated GPGPU  102 . 
     On completion of the data transfer for a workload/application  104  eviction, the GPGPU agent  124  requests the MMU  116  to free the now transferred portions/addresses of the GPGPU memory  112 , which were previously utilized by the now evicted workload/application  104 , and inform the proxy agent  122  about the completion of the transfer. In response, the proxy agent  122  terminates the connection/session with the GPGPU agent  124  for the old/evicted workload/application  104  and initiates a new connection/session with the GPGPU agent  124  for scheduling the new workload/application  104 . 
     When the old/evicted workload/application  104  tries to access the GPGPU  102  again, the proxy agent  122  notices that associated data of the workload/application  104  is not in the GPGPU memory  112  but is instead in the remote memory units  132  and buffers associated requests/commands in the virtual queue  136 . This determination may be made based on the table  200 , which includes the status  208  of the workloads/applications  104 . The proxy agent  122  identifies a new available GPGPU  102  (possibly requiring an eviction) and assigns the identified GPGPU  102  to the previously evicted workload/application  104 . As described above, this assignment includes the proxy agent  122  establishing a new session with the newly-allocated GPGPU agent  124  and provides the control page  134  to the GPGPU agent  124  along with a request to transfer data from the indicated portions of the remote memory units  132  into the new GPGPU memory  112  via the DMA unit  118 . 
     Upon completion of the data transfer from the remote memory units  132  to the new GPGPU memory  112 , the proxy agent  122  updates the mapping in the table  200  and copies any requests/commands from the virtual queue  136  that are associated with the workload/application  104  into command submission channels of the newly-assigned GPGPU  102 . Future requests/commands are forwarded to the new GPGPU. Further, upon completion of the data transfer from the remote memory units  132  to the new GPGPU memory  112 , the proxy agent  122  instructs the remote memory management unit  128  to free the memory addresses corresponding to the remote memory units  132  associated with the workload/application  104  newly-assigned to a GPGPU  102 . 
     As described above, monitoring information from monitoring agents  126  can be used to generate performance/usage profiles for workloads/application  104  that describe resources usage in relation to GPGPU  102  and associated GPGPU memory  112 . Using such workload/application profiles, the proxy agent  122  can predict when a workload/application  104  should be evicted from a GPGPU  102  (i.e., in response to predicting a low resource utilization), as well as when a workload/application  104  should be reallocated to a GPGPU  102  before a workload/application  104  makes a real-time GPGPU request. 
     In scenarios where a GPGPU  102  becomes available without eviction on the GPGPU node  110  from which a workload/application  104  was previously evicted (e.g., due to normal termination of a workload/application  104 ), the GPGPU agent  124  can inform the availability of GPGPU  102  resources to the proxy agent  122 . Thereafter, the proxy agent  122  can request the GPGPU agent  124  to pre-fetch the data of the evicted workload/application  104  from the remote memory units  132  and move this data into the GPGPU memory  112  of the newly-freed GPGPU  102 . 
     As described above and as will be described below, the data center system  100  assists in sharing resources of GPGPUs  102  more efficiently in cloud environments by allowing GPGPUs  102  to be oversubscribed for certain workloads/applications  104 . In particular, workloads/applications  104  allocated to GPGPUs  102  are monitored to build usage/performance profiles per workload/application  104 . These usage/performance profiles built for each workload/application  104  can be used for predicting or otherwise better determining when a workload/application  104  is underutilizing resources of GPGPUs  102 . When a usage/performance profile indicates that a workload/application  104  is underutilizing resources of a GPGPU  102  or will likely underutilize resources of GPGPUs  102  in the near future, the proxy agent  122  may mark the workload/application  102  for eviction from the current GPGPU  102 . When the workload/application  104  is to be processed again (e.g., a GPGPU request/command is received), the workload/application  104  can be dynamically reassigned to another GPGPU  102 . This dynamic movement of workloads/applications  104  between GPGPUs  102  removes the tight, static coupling of workloads/applications  104  to GPGPUs  102 . Further, through the use of emulated/virtual GPGPUs on compute nodes  108 , GPGPU  102  sharing is transparent to applications  104  while still facilitating GPGPU  102  time-sharing to reduce underutilization of GPGPUs  102 . 
     Turning now to  FIGS. 5A-5C , a method  500  will be described for managing placement/scheduling of workloads of applications  104  to GPGPUs  102  in the data center system  100 , according to one example embodiment. The operations in the flow diagrams will be described with reference to the exemplary embodiments of the other figures. However, it should be understood that the operations of the flow diagrams can be performed by embodiments of the invention other than those discussed with reference to the other figures, and the embodiments of the invention discussed with reference to these other figures can perform operations different than those discussed with reference to the flow diagrams. 
     As shown in  FIGS. 5A-5C , the method  500  may commence at operation  502  with an application  104  being initialized on a compute node  108 . For example, the application  104 A may be initialized on the compute node  108 A at operation  502 . The application  104 A initialized at operation  502  may be associated with a workload that the application  104 A is seeking to have performed on a GPGPU. As will be described below, the compute node  108 A may facilitate processing of the workload of the application  104 A through use of the GPGPUs  102  of the data center system  100 . Although the method  500  may be performed in relation to any application  104 , for purposes of explanation, the method  500  will be described in relation to the application  104 A. 
     At operation  504 , the application agent  120 A of the compute node  108 A emulates virtual GPGPUs for the application  104 A such that access to the remote GPGPUs  102  in the data center system  100  is transparent to the applications  104 A. In particular, the application agent  120 A provides an environment and set of libraries to assist the application  104 A to access GPGPUs  102  of the data center system  100  through an abstraction layer provided by emulated/virtual GPGPUs. 
     At operation  506 , a cloud orchestrator  106  of the data center system  100  allocates a proxy agent  122  for scheduling a workload of the application  104 A to a GPGPU  102  of the data center system  100 . The proxy agent  122  may be used for (1) scheduling/assigning applications  104  and associated workloads to GPGPUs  102  via corresponding GPGPU agents  124  of GPGPU nodes  110 , which monitor/manage the GPGPUs  102 , (2) evicting workloads/applications  104  from GPGPUs  102  based on monitored performance information/profiles of the workloads/applications  104 , and (3) rescheduling/reassigning evicted workloads/applications  104  to other GPGPUs  102  via corresponding GPGPU agents  124  that monitor/manage these other GPGPUs  102  (e.g., the GPGPU agent  124 A monitors the GPGPUs  102 A 1 - 102 A 3  and associated GPGPU memories  112 A 1 - 112 A 3 , while the GPGPU agent  124 Z monitors the GPGPUs  102 Z 1 - 102 Z 2  and associated GPGPU memories  112 Z 1 - 112 Z). 
     At operation  508 , the application agent  120 A and the proxy agent  122  establish a dedicated session/connection  136   1  for the workload/application  104 A. This session/connection  136   1  may be used for transferring requests/commands from the application  104 A, which describe details of a workload of the application  104 A to be assigned to a GPGPU  102  (e.g., an amount of GPGPU memory  112  and/or a desired/requested GPGPU  102  architecture). 
     At operation  510 , the proxy agent  122  selects a GPGPU  102  for the workload of the application  104 . The GPGPU  102  is selected from a set of GPGPUs  102  in the data center system  100  (e.g., all of the GPGPUs  102  in the data center system  100 ) and the selection is based on the available resources from the set of GPGPUs  102  (e.g., available GPGPU memory  112 ) and/or requirements of the workload/application  104 A. 
     At operation  512 , the proxy agent  122  establishes a session/connection for the workload/application  104 A with a GPGPU agent  124  of an associated GPGPU node  110  on which the selected GPGPU  102  resides. For example, the proxy agent  122  may select the GPGPU  102 A 1  of the GPGPU node  110 A at operation  510 . Based on this selection, the proxy agent  122  establishes a session/connection with the GPGPU agent  124 A for the workload/application  104 A at operation  512 . For example,  FIG. 6  shows a session/connection  602   1  between the proxy agent  122  and the GPGPU agent  124 A for the workload/application  104 A. This session/connection  602   1  may be used for transferring requests/commands from the application  104 A to the GPGPU agent  124 A via the proxy agent  122 . For purposes of explanation, the method  500  will be described in relation to the workload/application  104 A being assigned to the GPGPU  102 A 1  on the GPGPU node  110 A. 
     At operation  513 , the proxy agent  122  may update a status of the workload/application  104 A in the data center system  100 . In particular, the proxy agent  122  may update the status  208  in the table  200  shown in  FIG. 4  to note that the workload/application  104 A has been allocated/scheduled/assigned to the GPGPU  102 A 1 . 
     At operation  514 , the GPGPU agent  124 A receives a GPGPU request from the application  104 A. The GPGPU request is a request to process the workload of the application  104 A and includes details of processing the workload. As mentioned above, the application agent  120 A emulates a set of virtual GPGPUs that the application  104 A interacts (e.g., the application  104 A transmits GPGPU requests to these virtual GPGPUs). In response to interaction with these virtual GPGPUs, the application agent  120 A forwards the GPGPU requests to the proxy agent  122  for processing by an assigned/selected GPGPU  102 A 1  in the data center system  100 . 
     At operation  516 , the GPGPU  102 A 1  processes the indicated workload of the application  104 A based on the GPGPU request. This processing includes use of the associated GPGPU memory  112 A 1  attached or otherwise associated with the GPGPU  102 A 1 . 
     At operation  518 , the monitoring agent  126 A of the GPGPU agent  124 A monitors resources of the GPGPU  102 A 1  and/or the workload of the application  104 A being processed by the GPGPU  102 A 1 . In particular, the monitoring agent  126 A monitors and profiles the GPGPU  102 A 1 , including associated resources and workloads/applications  104  being processed by the GPGPU  102 A 1 . The monitoring information produced by the monitoring agent  126 A can be used to form usage/performance profiles for the workload/application  104 A that describe the performance/operation of workload of the application  104 A on the GPGPU  102 A 1 , including respective GPGPU memory  112 A usage. Although shown as a single operation, the monitoring agent  126 A may continually generate monitoring information to update the usage/performance profiles of the workload/application  104 . 
     At operation  520 , the proxy agent  122  may determine if an idle period for the workload/application  104 A on the GPGPU  102 A 1  is below a threshold usage/idle value. In particular, the proxy agent  122  may determine whether the workload/application  104 A, which is being processed by the GPGPU  102 A 1 , is efficiently using resources of the GPGPU  102 A 1  or is underutilizing resources of the GPGPU  102 A 1 . In some embodiments, the proxy agent  122  may use a usage/performance profile of the workload/application  104 A, which was generated based on monitoring information from the monitoring agent  126 A, to determine whether an idle period for the workload/application  104 A on the GPGPU  102 A 1  is below the threshold usage/idle value. In response to determining at operation  520  that an idle period for the workload/application  104 A on the GPGPU  102 A 1  is not below the threshold usage/idle value, the method  500  may return to operation  516  to continue processing the workload/application  104 A. Conversely, in response to determining at operation  520  that an idle period for the workload/application  104 A on the GPGPU  102 A 1  is below the threshold usage/idle value, the method  500  may move to operation  522 . 
     At operation  522 , the proxy agent  122  may designate the workload/application  104 A as a candidate for eviction from the GPGPU  102 A 1  and may add an identifier for this workload/application  104 A to a candidate list of workloads/applications  104  for eviction. In particular, since the workload/application  104 A is not efficiently utilizing resources of the GPGPU  102 A 1 , the proxy agent  122  may determine that this workload/application  104 A can be evicted from the GPGPU  102 A 1  in favor of a workload/application  104  that may more efficiently utilize resources of the GPGPU  102 A 1 . 
     At operation  524 , the proxy agent  122  may determine if there is a need to evict the workload/application  104 A from the GPGPU  102 A 1 . For example, in response to receipt of a GPGPU request from another application  104 , the proxy agent  122  may determine that there are no available GPGPUs  102  to handle the GPGPU request (i.e., a workload/application has been assigned to each GPGPU  102  in the data center system  100 ). Since the workload/application  104 A is underutilizing the GPGPU  102 A 1  (as determined at operation  522 ), the proxy agent  122  may determine at operation  524  that there is a need to evict the workload/application  104 A from the GPGPU  102 A 1 . In response to determining that there is not a need to evict the workload/application  104 A from the GPGPU  102 A 1 , the method  500  may return to operation  516  to continue processing the workload/application  104 A. Conversely, in response to determining that there is a need to evict the workload/application  104 A from the GPGPU  102 A 1 , the method  500  may move to operation  525 . In the example used herein, the workload/application  104 A is determined to be evicted from the GPGPU  102 A 1  at operation  524 . However, the proxy agent  122  may have indicated that several workloads/applications  104  are candidates to be evicted and the workload/application  104 A was selected because of (1) the degree of use of the associated GPGPU  102 A 1  (e.g., high idle time in relation to other candidate workloads/applications  104  for eviction), (2) resource similarities between the workload/application  104 A and the workload/application  104  that is to be assigned to a GPGPU  102 , (3) a lower priority of the candidate workload/application  104  than that of the workload/application  104  that is to be assigned to a GPGPU  102 , and/or (4) a round robin approach. 
     At operation  525 , the proxy agent  122  may update a status of the workload/application  104 A in the data center system  100 . In particular, the proxy agent  122  may update the status  208  in the table  200  shown in  FIG. 4  to note that the workload/application  104 A is being evicted to the disaggregated/global memory units  132 . 
     At operation  526 , GPGPU requests from the application  104 A, received after determining/selecting to evict the workload of the application  104 A from the GPGPU  102 A 1 , are buffered in a virtual queue  136  of the proxy agent  122  without forwarding them to the previously allocated GPGPU  102 A 1 . This buffering will maintain complete transparency of the eviction to the application  104 A on the compute node  108 A as requests/commands will not be lost during the data transfer. 
     At operation  528 , the GPGPU agent  124 A may prioritize existing/pending requests/commands associated with the workload/application  104 A that has been selected for eviction. These existing/pending requests/commands have already been received by the GPGPU agent  124 A and are to be completed by the GPGPU  102 A 1  before eviction can occur. In some embodiments, the proxy agent  122  may trigger or otherwise cause the GPGPU agent  124 A to prioritize existing/pending requests/commands. 
     At operation  530 , the proxy agent  122  may obtain from the remote memory management unit  128 , following completion of all pending requests/commands associated with the selected workload/application  104 A to be evicted, a range of destination memory addresses for transferring data of the evicted workload/application  104 A to remote memory units  132 . This range of destination memory addresses of the remote memory units  132  will be used for storing data of the workload/application  104 A until reassignment to another GPGPU  102 . 
     At operation  532 , the remote memory management unit  128  allocates a control page  134  to map source virtual addresses of the evicted workload/application  104 A to the corresponding destination addresses of the remote memory units  132 . Following population with virtual to physical address mappings, this control page  134  will serve as a guide for future use of data of the workload/application  104 A stored in the disaggregated/global memory units  132 . 
     At operation  534 , the GPGPU agent  124 A transfers data of the workload/application  104 A from the GPGPU memory  112 A 1  to the remote memory units  132  using the source virtual addresses of the GPGPU memory  112 A 1  and destination addresses of the disaggregated/global memory units  132 . Namely, the GPGPU agent  124 A transfers data of the workload/application  104 A from locations in the GPGPU memory  112 A 1  corresponding to the source virtual addresses to locations in the remote memory units  132  corresponding to the destination addresses. 
     At operation  536 , the remote memory management unit  128  updates the control page  134  based on the transfer of data of the workload/application  104 A. Namely, mappings are stored in the control page  134  to designate transfers of data between locations in the GPGPU memory  112 A 1  corresponding to the source virtual addresses to locations in the remote memory units  132  corresponding to the destination physical addresses. 
     At operation  538 , portions of the GPGPU memory  112 A allocated to the workload/application  104 A are freed or otherwise deallocated following transfer to the remote memory units  132 . Accordingly, these now freed/deallocated portions of the GPGPU memory  112 A can be used for another workload/application  104 . 
     At operation  540 , the GPGPU agent  124 A may report the completion of the data transfer/eviction of the workload/application  104 A from the GPGPU  102 A 1  to the proxy agent  122 . 
     At operation  542 , the proxy agent  122  may update a status of the workload/application  104 A in the data center system  100 . In particular, the proxy agent  122  may update the status  208  in the table  200  shown in  FIG. 4  to note that the workload/application  104 A has been evicted to the disaggregated/global memory units  132 . 
     At operation  544 , the proxy agent  122  may terminate the connection/session  602   1  with the GPGPU agent  124 A in relation to the evicted workload/application  104 A. In one embodiment, this termination is made in response to receipt from the GPGPU agent  124 A that the transfer/eviction of the workload/application  104 A from the GPGPU  102 A 1  has completed. 
     At operation  546 , the proxy agent  122  may continually determine if a request/command associated with the workload/application  104 A has been received. Upon receipt of a request request/command associated with the workload/application  104 A, the method  500  may move to operation  548 . 
     At operation  548 , the proxy agent  122  selects a GPGPU  102  for the previously evicted workload/application  104 A. Similar to operation  510 , the GPGPU  102  is selected from a set of GPGPUs  102  in the data center system  100  (e.g., all of the GPGPUs  102  in the data center system  100 ) and the selection is based on the available resources from the set of GPGPUs  102  (e.g., available GPGPU memory  112 ) and/or requirements of the workload/application  104 A. For example, the proxy agent  122  may select the GPGPU  102 Z 2  for the workload/application  104 A at operation  548 . 
     At operation  550 , the selected GPGPU  102  imports workload/application data associated with the workload/application  104 A from the remote memory units  132 , where the data was previously evited, to GPGPU memory  112  associated with the selected GPGPU  102  (e.g., the GPGPU memory  112 Z 2  when the GPGPU  102 Z 2  is selected at operation  548 ). In one embodiment this importation is performed based on or otherwise with consideration to the mappings of source virtual addresses of the evicted workload/application  104 A to the corresponding destination addresses of the remote memory units  132  stored in the control page  134 . 
     At operation  552 , entries in the control page  134  associated with the workload/application  104 A are removed such that associated space in the remote memory units  132  devoted to the workload/application  104 A is deallocated/freed. Thereafter, the method  500  may move to operation  512  for the proxy agent  122  to establish a session/connection with a GPGPU agent  124  of the selected GPGPU  102  for the workload/application  104 A. For example, when the GPGPU  102 Z 2  is selected at operation  548  for the workload/application  104 A, the proxy agent  122  establishes a session/connection  6022  between the proxy agent  122  and the GPGPU agent  124 Z for the workload/application  104 A, as shown in  FIG. 7 . 
     Turning now to  FIGS. 8A-8C , a method  800  will be described for managing placement/scheduling of workloads of applications  104  to GPGPUs  102  in the data center system  100 , according to one example embodiment. The operations in the flow diagrams will be described with reference to the exemplary embodiments of the other figures. However, it should be understood that the operations of the flow diagrams can be performed by embodiments of the invention other than those discussed with reference to the other figures, and the embodiments of the invention discussed with reference to these other figures can perform operations different than those discussed with reference to the flow diagrams. 
     As shown in  FIGS. 8A-8C , the method  800  may commence at operation  802  with the proxy agent  122  receiving a first GPGPU request from a first application  104 . The first GPGPU request requests the scheduling of a first workload of the first application  104  to a GPGPU  102  in a set of GPGPUs  102  of a data center system  100 . Hereinafter, the application  104 A will be used for purposes of illustration. 
     At operation  804 , the proxy agent  122  selects a first GPGPU  102  from the set of GPGPUs for processing the first workload of the first application  104 A based on one or more of (1) available resources of the set of GPGPUs  102  (e.g., the GPGPU memory  112 ) and (2) requirements of the workload as indicated by the first GPGPU request. Hereinafter, the method  800  will be described in relation to the proxy agent  122  selecting the GPGPU  102 A 1  for the first workload of the first application  104 A at operation  804 . 
     At operation  806 , the proxy agent  122  establishes (1) a first session  138   1  between an application agent  120 A located on a compute node  108 A on which the application  104 A is located and the proxy agent  122  and (2) a second session  602   1  between the first GPGPU  102 A 1  and the proxy agent  122  in response to selecting the first GPGPU  102 A 1  for the first workload to allow the first GPGPU  102 A 1  to process the first workload of the first application  104 A, including subsequent GPGPU requests associated with the first workload. 
     At operation  808 , the proxy agent  122  indicates in a workload mapping table  200 , that the first workload is scheduled to be processed by the first GPGPU  102 A 1  in response to selecting the first GPGPU  102 A 1  for the first workload. 
     At operation  810 , the proxy agent  122  collects a performance profile of the first workload on the first GPGPU  102 A 1  to describe usage of resources of the first GPGPU  102 A 1  by the first workload while the first GPGPU  102 A 1  is processing the first workload. 
     At operation  812 , the proxy agent  122  determines whether the performance profile of the first workload indicates that the usage of resources of the first GPGPU  102 A 1  by the first workload is below a threshold usage value. 
     At operation  814 , the proxy agent  122  adds an identifier of the first workload to a candidate list of workloads for eviction in response to determining that the performance profile of the first workload indicates that the usage of resources of the first GPGPU  102 A 1  by the first workload is below the threshold usage value. 
     At operation  816 , the proxy agent  122  receives a second GPGPU request from a second application  104 C, wherein the second GPGPU request requests scheduling of a second workload of the second application  104 C to a GPGPU  102  in the set of GPGPUs  102  in the data center system  100 . 
     At operation  818 , the proxy agent  122  determines that workloads have been assigned to all GPGPUs  102  in the set of GPGPUs  102 . 
     At operation  820 , the proxy agent  122  selects the first workload for eviction from the first GPGPU  102 A 1  in response to determining that workloads have been assigned to all GPGPUs  102  in the set of GPGPUs  102  and the first workload is included in the candidate list of workloads for eviction. In one embodiment, selecting the first workload for eviction is based on one or more of (1) a similarity between characteristics of the first workload and characteristics of the second workload, (2) a priority level of the first workload that is lower than a priority level of the second workload, and (3) a round robin approach. 
     At operation  822 , the proxy agent  122  triggers a GPGPU agent  124 A, which is located on a GPGPU node  110 A with the first GPGPU  102 A 1 , to evict the first workload from the first GPGPU  102 A 1 . 
     At operation  824 , the proxy agent  122  indicates in the workload mapping table  200  that the first workload is being evicted from the first GPGPU  102 A 1 . In one embodiment, eviction of the first workload from the first GPGPU  102 A 1  includes transferring all data of the first workload from resources (e.g., the GPGPU memory  112 A 1 ) of the first GPGPU  102 A 1  to remote memory  132  in the data center system  100  that is global to all GPGPUs  102  in the set of GPGPUs  102 . 
     At operation  826 , the proxy agent  122  indicates, in the workload mapping table  200 , that the first workload is not scheduled with a GPGPU  102  in the set of GPGPUs  102  and the data of the first workload is stored in the remote memory  132  in response to the data of the first workload being entirely moved into the remote memory  132 . 
     At operation  828 , the proxy agent  122  terminates the second session  602   1  between the first GPGPU  102 A 1  and the proxy agent  122 . 
     At operation  830 , the proxy agent  122  receives a third GPGPU request that is associated with the first workload. 
     At operation  832 , the proxy agent  122  determines that the first workload is not scheduled with a GPGPU  102  in the set of GPGPUs  102  based on the workload mapping table  200 . 
     At operation  834 , the proxy agent  122  buffers the second GPGPU request in a virtual queue  136  of the proxy agent  122  in response to determining that the first workload is not scheduled with a GPGPU  102  in the set of GPGPUs  102 . 
     At operation  836 , the proxy agent  122  selects a second GPGPU  102 A 2  from the set of GPGPUs  102  for processing the first workload of the application  104 A based on one or more of (1) available resources of the set of GPGPUs  102  and (2) requirements of the workload as indicated by the third GPGPU request. 
     At operation  838 , the proxy agent  122  triggers the transfer of data from the remote memory  132  to resources  112 A 2  of the second GPGPU  102 A 2 . 
     At operation  840 , the proxy agent  122  establishes a third session  6022  between the second GPGPU  102 A 2  and the proxy agent  122  in response to (1) selecting the second GPGPU  102 A 2  for the first workload to allow the second GPGPU  102 A 2  to process the first workload of the first application  104 A, including subsequent GPGPU requests associated with the first workload and (2) transferring the data from the remote memory  132  to the resources of the second GPGPU  102 A 2 . 
     At operation  842 , the proxy agent  122  collects a performance profile of the first workload on the second GPGPU  102 A 2  to describe usage of resources of the second GPGPU  102 A 2  by the first workload while the second GPGPU  102 A 2  is processing the first workload. In one embodiment, the first session  138   1  and the second session  602   1  are associated with the first workload of the first application  104 A. In one embodiment, the application agent  120 A is to emulate a set of virtual GPGPUs, and wherein the first application  104 A is to transfer the first GPGPU request and the third GPGPU request to the set of virtual GPGPUs such that the application agent  120 A can forward the first GPGPU request and the third GPGPU request to the proxy agent  122  via the first session  138   1 . 
     An electronic device stores and transmits (internally and/or with other electronic devices over a network) code (which is composed of software instructions and which is sometimes referred to as computer program code or a computer program) and/or data using machine-readable media (also called computer-readable media), such as machine-readable storage media (e.g., magnetic disks, optical disks, solid state drives, read only memory (ROM), flash memory devices, phase change memory) and machine-readable transmission media (also called a carrier) (e.g., electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals). Thus, an electronic device (e.g., a computer) includes hardware and software, such as a set of one or more processors (e.g., wherein a processor is a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application specific integrated circuit, field programmable gate array, other electronic circuitry, a combination of one or more of the preceding) coupled to one or more machine-readable storage media to store code for execution on the set of processors and/or to store data. For instance, an electronic device may include non-volatile memory containing the code since the non-volatile memory can persist code/data even when the electronic device is turned off (when power is removed), and while the electronic device is turned on that part of the code that is to be executed by the processor(s) of that electronic device is typically copied from the slower non-volatile memory into volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM)) of that electronic device. Typical electronic devices also include a set or one or more physical network interface(s) (NI(s)) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices. For example, the set of physical NIs (or the set of physical NI(s) in combination with the set of processors executing code) may perform any formatting, coding, or translating to allow the electronic device to send and receive data whether over a wired and/or a wireless connection. In some embodiments, a physical NI may comprise radio circuitry capable of receiving data from other electronic devices over a wireless connection and/or sending data out to other devices via a wireless connection. This radio circuitry may include transmitter(s), receiver(s), and/or transceiver(s) suitable for radiofrequency communication. The radio circuitry may convert digital data into a radio signal having the appropriate parameters (e.g., frequency, timing, channel, bandwidth, etc.). The radio signal may then be transmitted via antennas to the appropriate recipient(s). In some embodiments, the set of physical NI(s) may comprise network interface controller(s) (NICs), also known as a network interface card, network adapter, or local area network (LAN) adapter. The NIC(s) may facilitate in connecting the electronic device to other electronic devices allowing them to communicate via wire through plugging in a cable to a physical port connected to a NIC. One or more parts of an embodiment of the invention may be implemented using different combinations of software, firmware, and/or hardware. 
     A network device (ND) is an electronic device that communicatively interconnects other electronic devices on the network (e.g., other network devices, end-user devices). Some network devices are “multiple services network devices” that provide support for multiple networking functions (e.g., routing, bridging, switching, Layer 2 aggregation, session border control, Quality of Service, and/or subscriber management), and/or provide support for multiple application services (e.g., data, voice, and video). 
       FIG. 9A  illustrates connectivity between network devices (NDs) within an exemplary network, as well as three exemplary implementations of the NDs, according to some embodiments of the invention.  FIG. 9A  shows NDs  900 A-H, and their connectivity by way of lines between  900 A- 900 B,  900 B- 900 C,  900 C- 900 D,  900 D- 900 E,  900 E- 900 F,  900 F- 900 G, and  900 A- 900 G, as well as between  900 H and each of  900 A,  900 C,  900 D, and  900 G. These NDs are physical devices, and the connectivity between these NDs can be wireless or wired (often referred to as a link). An additional line extending from NDs  900 A,  900 E, and  900 F illustrates that these NDs act as ingress and egress points for the network (and thus, these NDs are sometimes referred to as edge NDs; while the other NDs may be called core NDs). 
     Two of the exemplary ND implementations in  FIG. 9A  are: 1) a special-purpose network device  902  that uses custom application-specific integrated-circuits (ASICs) and a special-purpose operating system (OS); and 2) a general purpose network device  904  that uses common off-the-shelf (COTS) processors and a standard OS. 
     The special-purpose network device  902  includes networking hardware  910  comprising a set of one or more processor(s)  912 , forwarding resource(s)  914  (which typically include one or more ASICs and/or network processors), and physical network interfaces (NIs)  916  (through which network connections are made, such as those shown by the connectivity between NDs  900 A-H), as well as non-transitory machine readable storage media  918  having stored therein networking software  920 . During operation, the networking software  920  may be executed by the networking hardware  910  to instantiate a set of one or more networking software instance(s)  922 . Each of the networking software instance(s)  922 , and that part of the networking hardware  910  that executes that network software instance (be it hardware dedicated to that networking software instance and/or time slices of hardware temporally shared by that networking software instance with others of the networking software instance(s)  922 ), form a separate virtual network element  930 A-R. Each of the virtual network element(s) (VNEs)  930 A-R includes a control communication and configuration module  932 A-R (sometimes referred to as a local control module or control communication module) and forwarding table(s)  934 A-R, such that a given virtual network element (e.g.,  930 A) includes the control communication and configuration module (e.g.,  932 A), a set of one or more forwarding table(s) (e.g.,  934 A), and that portion of the networking hardware  910  that executes the virtual network element (e.g.,  930 A). 
     The special-purpose network device  902  is often physically and/or logically considered to include: 1) a ND control plane  924  (sometimes referred to as a control plane) comprising the processor(s)  912  that execute the control communication and configuration module(s)  932 A-R; and 2) a ND forwarding plane  926  (sometimes referred to as a forwarding plane, a data plane, or a media plane) comprising the forwarding resource(s)  914  that utilize the forwarding table(s)  934 A-R and the physical NIs  916 . By way of example, where the ND is a router (or is implementing routing functionality), the ND control plane  924  (the processor(s)  912  executing the control communication and configuration module(s)  932 A-R) is typically responsible for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) and storing that routing information in the forwarding table(s)  934 A-R, and the ND forwarding plane  926  is responsible for receiving that data on the physical NIs  916  and forwarding that data out the appropriate ones of the physical NIs  916  based on the forwarding table(s)  934 A-R. 
       FIG. 9B  illustrates an exemplary way to implement the special-purpose network device  902  according to some embodiments of the invention.  FIG. 9B  shows a special-purpose network device including cards  938  (typically hot pluggable). While in some embodiments the cards  938  are of two types (one or more that operate as the ND forwarding plane  926  (sometimes called line cards), and one or more that operate to implement the ND control plane  924  (sometimes called control cards)), alternative embodiments may combine functionality onto a single card and/or include additional card types (e.g., one additional type of card is called a service card, resource card, or multi-application card). A service card can provide specialized processing (e.g., Layer 4 to Layer 7 services (e.g., firewall, Internet Protocol Security (IPsec), Secure Sockets Layer (SSL)/Transport Layer Security (TLS), Intrusion Detection System (IDS), peer-to-peer (P2P), Voice over IP (VoIP) Session Border Controller, Mobile Wireless Gateways (Gateway General Packet Radio Service (GPRS) Support Node (GGSN), Evolved Packet Core (EPC) Gateway)). By way of example, a service card may be used to terminate IPsec tunnels and execute the attendant authentication and encryption algorithms. These cards are coupled together through one or more interconnect mechanisms illustrated as backplane  936  (e.g., a first full mesh coupling the line cards and a second full mesh coupling all of the cards). 
     Returning to  FIG. 9A , the general purpose network device  904  includes hardware  940  comprising a set of one or more processor(s)  942  (which are often COTS processors) and physical NIs  946 , as well as non-transitory machine readable storage media  948  having stored therein software  950 , a cloud orchestrator  106 , a GPGPU agent(s)  124 , a GPGPU(s)  102 , an application  104 , remote memory management unit  128 , and/or a proxy agent  122 . During operation, the processor(s)  942  execute the software  950  to instantiate one or more sets of one or more applications  964 A-R. While one embodiment does not implement virtualization, alternative embodiments may use different forms of virtualization. For example, in one such alternative embodiment the virtualization layer  954  represents the kernel of an operating system (or a shim executing on a base operating system) that allows for the creation of multiple instances  962 A-R called software containers that may each be used to execute one (or more) of the sets of applications  964 A-R; where the multiple software containers (also called virtualization engines, virtual private servers, or jails) are user spaces (typically a virtual memory space) that are separate from each other and separate from the kernel space in which the operating system is run; and where the set of applications running in a given user space, unless explicitly allowed, cannot access the memory of the other processes. In another such alternative embodiment the virtualization layer  954  represents a hypervisor (sometimes referred to as a virtual machine monitor (VMM)) or a hypervisor executing on top of a host operating system, and each of the sets of applications  964 A-R is run on top of a guest operating system within an instance  962 A-R called a virtual machine (which may in some cases be considered a tightly isolated form of software container) that is run on top of the hypervisor—the guest operating system and application may not know they are running on a virtual machine as opposed to running on a “bare metal” host electronic device, or through para-virtualization the operating system and/or application may be aware of the presence of virtualization for optimization purposes. In yet other alternative embodiments, one, some or all of the applications are implemented as unikernel(s), which can be generated by compiling directly with an application only a limited set of libraries (e.g., from a library operating system (LibOS) including drivers/libraries of OS services) that provide the particular OS services needed by the application. As a unikernel can be implemented to run directly on hardware  940 , directly on a hypervisor (in which case the unikernel is sometimes described as running within a LibOS virtual machine), or in a software container, embodiments can be implemented fully with unikernels running directly on a hypervisor represented by virtualization layer  954 , unikernels running within software containers represented by instances  962 A-R, or as a combination of unikernels and the above-described techniques (e.g., unikernels and virtual machines both run directly on a hypervisor, unikernels and sets of applications that are run in different software containers). 
     The instantiation of the one or more sets of one or more applications  964 A-R, as well as virtualization if implemented, are collectively referred to as software instance(s)  952 . Each set of applications  964 A-R, corresponding virtualization construct (e.g., instance  962 A-R) if implemented, and that part of the hardware  940  that executes them (be it hardware dedicated to that execution and/or time slices of hardware temporally shared), forms a separate virtual network element(s)  960 A-R. 
     The virtual network element(s)  960 A-R perform similar functionality to the virtual network element(s)  930 A-R—e.g., similar to the control communication and configuration module(s)  932 A and forwarding table(s)  934 A (this virtualization of the hardware  940  is sometimes referred to as network function virtualization (NFV)). Thus, NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which could be located in Data centers, NDs, and customer premise equipment (CPE). While embodiments of the invention are illustrated with each instance  962 A-R corresponding to one VNE  960 A-R, alternative embodiments may implement this correspondence at a finer level granularity (e.g., line card virtual machines virtualize line cards, control card virtual machine virtualize control cards, etc.); it should be understood that the techniques described herein with reference to a correspondence of instances  962 A-R to VNEs also apply to embodiments where such a finer level of granularity and/or unikernels are used. 
     In certain embodiments, the virtualization layer  954  includes a virtual switch that provides similar forwarding services as a physical Ethernet switch. Specifically, this virtual switch forwards traffic between instances  962 A-R and the physical NI(s)  946 , as well as optionally between the instances  962 A-R; in addition, this virtual switch may enforce network isolation between the VNEs  960 A-R that by policy are not permitted to communicate with each other (e.g., by honoring virtual local area networks (VLANs)). 
     The third exemplary ND implementation in  FIG. 9A  is a hybrid network device  906 , which includes both custom ASICs/special-purpose OS and COTS processors/standard OS in a single ND or a single card within an ND. In certain embodiments of such a hybrid network device, a platform VM (i.e., a VM that that implements the functionality of the special-purpose network device  902 ) could provide for para-virtualization to the networking hardware present in the hybrid network device  906 . 
     Regardless of the above exemplary implementations of an ND, when a single one of multiple VNEs implemented by an ND is being considered (e.g., only one of the VNEs is part of a given virtual network) or where only a single VNE is currently being implemented by an ND, the shortened term network element (NE) is sometimes used to refer to that VNE. Also in all of the above exemplary implementations, each of the VNEs (e.g., VNE(s)  930 A-R, VNEs  960 A-R, and those in the hybrid network device  906 ) receives data on the physical NIs (e.g.,  916 ,  946 ) and forwards that data out the appropriate ones of the physical NIs (e.g.,  916 ,  946 ). For example, a VNE implementing IP router functionality forwards IP packets on the basis of some of the IP header information in the IP packet; where IP header information includes source IP address, destination IP address, source port, destination port (where “source port” and “destination port” refer herein to protocol ports, as opposed to physical ports of a ND), transport protocol (e.g., user datagram protocol (UDP), Transmission Control Protocol (TCP), and differentiated services code point (DSCP) values. 
       FIG. 9C  illustrates various exemplary ways in which VNEs may be coupled according to some embodiments of the invention.  FIG. 9C  shows VNEs  970 A. 1 - 970 A.P (and optionally VNEs  970 A.Q- 970 A.R) implemented in ND  900 A and VNE  970 H. 1  in ND  900 H. In  FIG. 9C , VNEs  970 A. 1 -P are separate from each other in the sense that they can receive packets from outside ND  900 A and forward packets outside of ND  900 A; VNE  970 A. 1  is coupled with VNE  970 H. 1 , and thus they communicate packets between their respective NDs; VNE  970 A. 2 - 970 A. 3  may optionally forward packets between themselves without forwarding them outside of the ND  900 A; and VNE  970 A.P may optionally be the first in a chain of VNEs that includes VNE  970 A.Q followed by VNE  970 A.R (this is sometimes referred to as dynamic service chaining, where each of the VNEs in the series of VNEs provides a different service—e.g., one or more layer 4-7 network services). While  FIG. 9C  illustrates various exemplary relationships between the VNEs, alternative embodiments may support other relationships (e.g., more/fewer VNEs, more/fewer dynamic service chains, multiple different dynamic service chains with some common VNEs and some different VNEs). 
     The NDs of  FIG. 9A , for example, may form part of the Internet or a private network; and other electronic devices (not shown; such as end user devices including workstations, laptops, netbooks, tablets, palm tops, mobile phones, smartphones, phablets, multimedia phones, Voice Over Internet Protocol (VOIP) phones, terminals, portable media players, GPS units, wearable devices, gaming systems, set-top boxes, Internet enabled household appliances) may be coupled to the network (directly or through other networks such as access networks) to communicate over the network (e.g., the Internet or virtual private networks (VPNs) overlaid on (e.g., tunneled through) the Internet) with each other (directly or through servers) and/or access content and/or services. Such content and/or services are typically provided by one or more servers (not shown) belonging to a service/content provider or one or more end user devices (not shown) participating in a peer-to-peer (P2P) service, and may include, for example, public webpages (e.g., free content, store fronts, search services), private webpages (e.g., username/password accessed webpages providing email services), and/or corporate networks over VPNs. For instance, end user devices may be coupled (e.g., through customer premise equipment coupled to an access network (wired or wirelessly)) to edge NDs, which are coupled (e.g., through one or more core NDs) to other edge NDs, which are coupled to electronic devices acting as servers. However, through compute and storage virtualization, one or more of the electronic devices operating as the NDs in  FIG. 9A  may also host one or more such servers (e.g., in the case of the general purpose network device  904 , one or more of the software instances  962 A-R may operate as servers; the same would be true for the hybrid network device  906 ; in the case of the special-purpose network device  902 , one or more such servers could also be run on a virtualization layer executed by the processor(s)  912 ); in which case the servers are said to be co-located with the VNEs of that ND. 
     A virtual network is a logical abstraction of a physical network (such as that in  FIG. 9A ) that provides network services (e.g., L2 and/or L3 services). A virtual network can be implemented as an overlay network (sometimes referred to as a network virtualization overlay) that provides network services (e.g., layer 2 (L2, data link layer) and/or layer 3 (L3, network layer) services) over an underlay network (e.g., an L3 network, such as an Internet Protocol (IP) network that uses tunnels (e.g., generic routing encapsulation (GRE), layer 2 tunneling protocol (L2TP), IPSec) to create the overlay network). 
     A network virtualization edge (NVE) sits at the edge of the underlay network and participates in implementing the network virtualization; the network-facing side of the NVE uses the underlay network to tunnel frames to and from other NVEs; the outward-facing side of the NVE sends and receives data to and from systems outside the network. A virtual network instance (VNI) is a specific instance of a virtual network on a NVE (e.g., a NE/VNE on an ND, a part of a NE/VNE on a ND where that NE/VNE is divided into multiple VNEs through emulation); one or more VNIs can be instantiated on an NVE (e.g., as different VNEs on an ND). A virtual access point (VAP) is a logical connection point on the NVE for connecting external systems to a virtual network; a VAP can be physical or virtual ports identified through logical interface identifiers (e.g., a VLAN ID). 
     Examples of network services include: 1) an Ethernet LAN emulation service (an Ethernet-based multipoint service similar to an Internet Engineering Task Force (IETF) Multiprotocol Label Switching (MPLS) or Ethernet VPN (EVPN) service) in which external systems are interconnected across the network by a LAN environment over the underlay network (e.g., an NVE provides separate L2 VNIs (virtual switching instances) for different such virtual networks, and L3 (e.g., IP/MPLS) tunneling encapsulation across the underlay network); and 2) a virtualized IP forwarding service (similar to IETF IP VPN (e.g., Border Gateway Protocol (BGP)/MPLS IPVPN) from a service definition perspective) in which external systems are interconnected across the network by an L3 environment over the underlay network (e.g., an NVE provides separate L3 VNIs (forwarding and routing instances) for different such virtual networks, and L3 (e.g., IP/MPLS) tunneling encapsulation across the underlay network)). Network services may also include quality of service capabilities (e.g., traffic classification marking, traffic conditioning and scheduling), security capabilities (e.g., filters to protect customer premises from network—originated attacks, to avoid malformed route announcements), and management capabilities (e.g., full detection and processing). 
       FIG. 9D  illustrates a network with a single network element on each of the NDs of  FIG. 9A , and within this straight forward approach contrasts a traditional distributed approach (commonly used by traditional routers) with a centralized approach for maintaining reachability and forwarding information (also called network control), according to some embodiments of the invention. Specifically,  FIG. 9D  illustrates network elements (NEs)  970 A-H with the same connectivity as the NDs  900 A-H of  FIG. 9A . 
       FIG. 9D  illustrates that the distributed approach  972  distributes responsibility for generating the reachability and forwarding information across the NEs  970 A-H; in other words, the process of neighbor discovery and topology discovery is distributed. 
     For example, where the special-purpose network device  902  is used, the control communication and configuration module(s)  932 A-R of the ND control plane  924  typically include a reachability and forwarding information module to implement one or more routing protocols (e.g., an exterior gateway protocol such as Border Gateway Protocol (BGP), Interior Gateway Protocol(s) (IGP) (e.g., Open Shortest Path First (OSPF), Intermediate System to Intermediate System (IS-IS), Routing Information Protocol (RIP), Label Distribution Protocol (LDP), Resource Reservation Protocol (RSVP) (including RSVP-Traffic Engineering (TE): Extensions to RSVP for LSP Tunnels and Generalized Multi-Protocol Label Switching (GMPLS) Signaling RSVP-TE)) that communicate with other NEs to exchange routes, and then selects those routes based on one or more routing metrics. Thus, the NEs  970 A-H (e.g., the processor(s)  912  executing the control communication and configuration module(s)  932 A-R) perform their responsibility for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) by distributively determining the reachability within the network and calculating their respective forwarding information. Routes and adjacencies are stored in one or more routing structures (e.g., Routing Information Base (RIB), Label Information Base (LIB), one or more adjacency structures) on the ND control plane  924 . The ND control plane  924  programs the ND forwarding plane  926  with information (e.g., adjacency and route information) based on the routing structure(s). For example, the ND control plane  924  programs the adjacency and route information into one or more forwarding table(s)  934 A-R (e.g., Forwarding Information Base (FIB), Label Forwarding Information Base (LFIB), and one or more adjacency structures) on the ND forwarding plane  926 . For layer 2 forwarding, the ND can store one or more bridging tables that are used to forward data based on the layer 2 information in that data. While the above example uses the special-purpose network device  902 , the same distributed approach  972  can be implemented on the general purpose network device  904  and the hybrid network device  906 . 
       FIG. 9D  illustrates that a centralized approach  974  (also known as software defined networking (SDN)) that decouples the system that makes decisions about where traffic is sent from the underlying systems that forwards traffic to the selected destination. The illustrated centralized approach  974  has the responsibility for the generation of reachability and forwarding information in a centralized control plane  976  (sometimes referred to as a SDN control module, controller, network controller, OpenFlow controller, SDN controller, control plane node, network virtualization authority, or management control entity), and thus the process of neighbor discovery and topology discovery is centralized. The centralized control plane  976  has a south bound interface  982  with a data plane  980  (sometime referred to the infrastructure layer, network forwarding plane, or forwarding plane (which should not be confused with a ND forwarding plane)) that includes the NEs  970 A-H (sometimes referred to as switches, forwarding elements, data plane elements, or nodes). The centralized control plane  976  includes a network controller  978 , which includes a centralized reachability and forwarding information module  979  that determines the reachability within the network and distributes the forwarding information to the NEs  970 A-H of the data plane  980  over the south bound interface  982  (which may use the OpenFlow protocol). Thus, the network intelligence is centralized in the centralized control plane  976  executing on electronic devices that are typically separate from the NDs. 
     For example, where the special-purpose network device  902  is used in the data plane  980 , each of the control communication and configuration module(s)  932 A-R of the ND control plane  924  typically include a control agent that provides the VNE side of the south bound interface  982 . In this case, the ND control plane  924  (the processor(s)  912  executing the control communication and configuration module(s)  932 A-R) performs its responsibility for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) through the control agent communicating with the centralized control plane  976  to receive the forwarding information (and in some cases, the reachability information) from the centralized reachability and forwarding information module  979  (it should be understood that in some embodiments of the invention, the control communication and configuration module(s)  932 A-R, in addition to communicating with the centralized control plane  976 , may also play some role in determining reachability and/or calculating forwarding information—albeit less so than in the case of a distributed approach; such embodiments are generally considered to fall under the centralized approach  974 , but may also be considered a hybrid approach). 
     While the above example uses the special-purpose network device  902 , the same centralized approach  974  can be implemented with the general purpose network device  904  (e.g., each of the VNE  960 A-R performs its responsibility for controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) by communicating with the centralized control plane  976  to receive the forwarding information (and in some cases, the reachability information) from the centralized reachability and forwarding information module  979 ; it should be understood that in some embodiments of the invention, the VNEs  960 A-R, in addition to communicating with the centralized control plane  976 , may also play some role in determining reachability and/or calculating forwarding information—albeit less so than in the case of a distributed approach) and the hybrid network device  906 . In fact, the use of SDN techniques can enhance the NFV techniques typically used in the general purpose network device  904  or hybrid network device  906  implementations as NFV is able to support SDN by providing an infrastructure upon which the SDN software can be run, and NFV and SDN both aim to make use of commodity server hardware and physical switches. 
       FIG. 9D  also shows that the centralized control plane  976  has a north bound interface  984  to an application layer  986 , in which resides application(s)  988 , a cloud orchestrator  106 , a GPGPU agent(s)  124 , a GPGPU(s)  102 , an application  104 , remote memory management unit  128 , and/or a proxy agent  122 . The centralized control plane  976  has the ability to form virtual networks  992  (sometimes referred to as a logical forwarding plane, network services, or overlay networks (with the NEs  970 A-H of the data plane  980  being the underlay network)) for the application(s)  988 , a cloud orchestrator  106 , a GPGPU agent(s)  124 , a GPGPU(s)  102 , an application  104 , remote memory management unit  128 , and/or a proxy agent  122 . Thus, the centralized control plane  976  maintains a global view of all NDs and configured NEs/VNEs, and it maps the virtual networks to the underlying NDs efficiently (including maintaining these mappings as the physical network changes either through hardware (ND, link, or ND component) failure, addition, or removal). 
     While  FIG. 9D  shows the distributed approach  972  separate from the centralized approach  974 , the effort of network control may be distributed differently or the two combined in certain embodiments of the invention. For example: 1) embodiments may generally use the centralized approach (SDN)  974 , but have certain functions delegated to the NEs (e.g., the distributed approach may be used to implement one or more of fault monitoring, performance monitoring, protection switching, and primitives for neighbor and/or topology discovery); or 2) embodiments of the invention may perform neighbor discovery and topology discovery via both the centralized control plane and the distributed protocols, and the results compared to raise exceptions where they do not agree. Such embodiments are generally considered to fall under the centralized approach  974 , but may also be considered a hybrid approach. 
     While  FIG. 9D  illustrates the simple case where each of the NDs  900 A-H implements a single NE  970 A-H, it should be understood that the network control approaches described with reference to  FIG. 9D  also work for networks where one or more of the NDs  900 A-H implement multiple VNEs (e.g., VNEs  930 A-R, VNEs  960 A-R, those in the hybrid network device  906 ). Alternatively or in addition, the network controller  978  may also emulate the implementation of multiple VNEs in a single ND. Specifically, instead of (or in addition to) implementing multiple VNEs in a single ND, the network controller  978  may present the implementation of a VNE/NE in a single ND as multiple VNEs in the virtual networks  992  (all in the same one of the virtual network(s)  992 , each in different ones of the virtual network(s)  992 , or some combination). For example, the network controller  978  may cause an ND to implement a single VNE (a NE) in the underlay network, and then logically divide up the resources of that NE within the centralized control plane  976  to present different VNEs in the virtual network(s)  992  (where these different VNEs in the overlay networks are sharing the resources of the single VNE/NE implementation on the ND in the underlay network). 
     On the other hand,  FIGS. 9E and 9F  respectively illustrate exemplary abstractions of NEs and VNEs that the network controller  978  may present as part of different ones of the virtual networks  992 .  FIG. 9E  illustrates the simple case of where each of the NDs  900 A-H implements a single NE  970 A-H (see  FIG. 9D ), but the centralized control plane  976  has abstracted multiple of the NEs in different NDs (the NEs  970 A-C and G-H) into (to represent) a single NE  9701  in one of the virtual network(s)  992  of  FIG. 9D , according to some embodiments of the invention.  FIG. 9E  shows that in this virtual network, the NE  9701  is coupled to NE  970 D and  970 F, which are both still coupled to NE  970 E. 
       FIG. 9F  illustrates a case where multiple VNEs (VNE  970 A. 1  and VNE  970 H. 1 ) are implemented on different NDs (ND  900 A and ND  900 H) and are coupled to each other, and where the centralized control plane  976  has abstracted these multiple VNEs such that they appear as a single VNE  970 T within one of the virtual networks  992  of  FIG. 9D , according to some embodiments of the invention. Thus, the abstraction of a NE or VNE can span multiple NDs. 
     While some embodiments of the invention implement the centralized control plane  976  as a single entity (e.g., a single instance of software running on a single electronic device), alternative embodiments may spread the functionality across multiple entities for redundancy and/or scalability purposes (e.g., multiple instances of software running on different electronic devices). 
     Similar to the network device implementations, the electronic device(s) running the centralized control plane  976 , and thus the network controller  978  including the centralized reachability and forwarding information module  979 , may be implemented a variety of ways (e.g., a special purpose device, a general-purpose (e.g., COTS) device, or hybrid device). These electronic device(s) would similarly include processor(s), a set or one or more physical NIs, and a non-transitory machine-readable storage medium having stored thereon the centralized control plane software. For instance,  FIG. 10  illustrates a general purpose control plane device  1004  including hardware  1040  comprising a set of one or more processor(s)  1042  (which are often COTS processors) and physical NIs  1046 , as well as non-transitory machine readable storage media  1048  having stored therein centralized control plane (CCP) software  1050 , a cloud orchestrator  106 , a GPGPU agent(s)  124 , a GPGPU(s)  102 , an application  104 , remote memory management unit  128 , and/or a proxy agent  122 . 
     In embodiments that use compute virtualization, the processor(s)  1042  typically execute software to instantiate a virtualization layer  1054  (e.g., in one embodiment the virtualization layer  1054  represents the kernel of an operating system (or a shim executing on a base operating system) that allows for the creation of multiple instances  1062 A-R called software containers (representing separate user spaces and also called virtualization engines, virtual private servers, or jails) that may each be used to execute a set of one or more applications; in another embodiment the virtualization layer  1054  represents a hypervisor (sometimes referred to as a virtual machine monitor (VMM)) or a hypervisor executing on top of a host operating system, and an application is run on top of a guest operating system within an instance  1062 A-R called a virtual machine (which in some cases may be considered a tightly isolated form of software container) that is run by the hypervisor; in another embodiment, an application is implemented as a unikernel, which can be generated by compiling directly with an application only a limited set of libraries (e.g., from a library operating system (LibOS) including drivers/libraries of OS services) that provide the particular OS services needed by the application, and the unikernel can run directly on hardware  1040 , directly on a hypervisor represented by virtualization layer  1054  (in which case the unikernel is sometimes described as running within a LibOS virtual machine), or in a software container represented by one of instances  1062 A-R). Again, in embodiments where compute virtualization is used, during operation an instance of the CCP software  1050  (illustrated as CCP instance  1076 A) is executed (e.g., within the instance  1062 A) on the virtualization layer  1054 . In embodiments where compute virtualization is not used, the CCP instance  1076 A is executed, as a unikernel or on top of a host operating system, on the “bare metal” general purpose control plane device  1004 . The instantiation of the CCP instance  1076 A, as well as the virtualization layer  1054  and instances  1062 A-R if implemented, are collectively referred to as software instance(s)  1052 . 
     In some embodiments, the CCP instance  1076 A includes a network controller instance  1078 . The network controller instance  1078  includes a centralized reachability and forwarding information module instance  1079  (which is a middleware layer providing the context of the network controller  978  to the operating system and communicating with the various NEs), and an CCP application layer  1080  (sometimes referred to as an application layer) over the middleware layer (providing the intelligence required for various network operations such as protocols, network situational awareness, and user-interfaces). At a more abstract level, this CCP application layer  1080  within the centralized control plane  976  works with virtual network view(s) (logical view(s) of the network) and the middleware layer provides the conversion from the virtual networks to the physical view. 
     The centralized control plane  976  transmits relevant messages to the data plane  980  based on CCP application layer  1080  calculations and middleware layer mapping for each flow. A flow may be defined as a set of packets whose headers match a given pattern of bits; in this sense, traditional IP forwarding is also flow-based forwarding where the flows are defined by the destination IP address for example; however, in other implementations, the given pattern of bits used for a flow definition may include more fields (e.g., 10 or more) in the packet headers. Different NDs/NEs/VNEs of the data plane  980  may receive different messages, and thus different forwarding information. The data plane  980  processes these messages and programs the appropriate flow information and corresponding actions in the forwarding tables (sometime referred to as flow tables) of the appropriate NE/VNEs, and then the NEs/VNEs map incoming packets to flows represented in the forwarding tables and forward packets based on the matches in the forwarding tables. 
     Standards such as OpenFlow define the protocols used for the messages, as well as a model for processing the packets. The model for processing packets includes header parsing, packet classification, and making forwarding decisions. Header parsing describes how to interpret a packet based upon a well-known set of protocols. Some protocol fields are used to build a match structure (or key) that will be used in packet classification (e.g., a first key field could be a source media access control (MAC) address, and a second key field could be a destination MAC address). 
     Packet classification involves executing a lookup in memory to classify the packet by determining which entry (also referred to as a forwarding table entry or flow entry) in the forwarding tables best matches the packet based upon the match structure, or key, of the forwarding table entries. It is possible that many flows represented in the forwarding table entries can correspond/match to a packet; in this case the system is typically configured to determine one forwarding table entry from the many according to a defined scheme (e.g., selecting a first forwarding table entry that is matched). Forwarding table entries include both a specific set of match criteria (a set of values or wildcards, or an indication of what portions of a packet should be compared to a particular value/values/wildcards, as defined by the matching capabilities—for specific fields in the packet header, or for some other packet content), and a set of one or more actions for the data plane to take on receiving a matching packet. For example, an action may be to push a header onto the packet, for the packet using a particular port, flood the packet, or simply drop the packet. Thus, a forwarding table entry for IPv4/IPv6 packets with a particular transmission control protocol (TCP) destination port could contain an action specifying that these packets should be dropped. 
     Making forwarding decisions and performing actions occurs, based upon the forwarding table entry identified during packet classification, by executing the set of actions identified in the matched forwarding table entry on the packet. 
     However, when an unknown packet (for example, a “missed packet” or a “match-miss” as used in OpenFlow parlance) arrives at the data plane  980 , the packet (or a subset of the packet header and content) is typically forwarded to the centralized control plane  976 . The centralized control plane  976  will then program forwarding table entries into the data plane  980  to accommodate packets belonging to the flow of the unknown packet. Once a specific forwarding table entry has been programmed into the data plane  980  by the centralized control plane  976 , the next packet with matching credentials will match that forwarding table entry and take the set of actions associated with that matched entry. 
     A network interface (NI) may be physical or virtual; and in the context of IP, an interface address is an IP address assigned to a NI, be it a physical NI or virtual NI. A virtual NI may be associated with a physical NI, with another virtual interface, or stand on its own (e.g., a loopback interface, a point-to-point protocol interface). A NI (physical or virtual) may be numbered (a NI with an IP address) or unnumbered (a NI without an IP address). A loopback interface (and its loopback address) is a specific type of virtual NI (and IP address) of a NE/VNE (physical or virtual) often used for management purposes; where such an IP address is referred to as the nodal loopback address. The IP address(es) assigned to the NI(s) of a ND are referred to as IP addresses of that ND; at a more granular level, the IP address(es) assigned to NI(s) assigned to a NE/VNE implemented on a ND can be referred to as IP addresses of that NE/VNE. 
     Next hop selection by the routing system for a given destination may resolve to one path (that is, a routing protocol may generate one next hop on a shortest path); but if the routing system determines there are multiple viable next hops (that is, the routing protocol generated forwarding solution offers more than one next hop on a shortest path—multiple equal cost next hops), some additional criteria is used—for instance, in a connectionless network, Equal Cost Multi Path (ECMP) (also known as Equal Cost Multi Pathing, multipath forwarding and IP multipath) may be used (e.g., typical implementations use as the criteria particular header fields to ensure that the packets of a particular packet flow are always forwarded on the same next hop to preserve packet flow ordering). For purposes of multipath forwarding, a packet flow is defined as a set of packets that share an ordering constraint. As an example, the set of packets in a particular TCP transfer sequence need to arrive in order, else the TCP logic will interpret the out of order delivery as congestion and slow the TCP transfer rate down. 
     A Layer 3 (L3) Link Aggregation (LAG) link is a link directly connecting two NDs with multiple IP-addressed link paths (each link path is assigned a different IP address), and a load distribution decision across these different link paths is performed at the ND forwarding plane; in which case, a load distribution decision is made between the link paths. 
     Some NDs include functionality for authentication, authorization, and accounting (AAA) protocols (e.g., RADIUS (Remote Authentication Dial-In User Service), Diameter, and/or TACACS+ (Terminal Access Controller Access Control System Plus). AAA can be provided through a client/server model, where the AAA client is implemented on a ND and the AAA server can be implemented either locally on the ND or on a remote electronic device coupled with the ND. Authentication is the process of identifying and verifying a subscriber. For instance, a subscriber might be identified by a combination of a username and a password or through a unique key. Authorization determines what a subscriber can do after being authenticated, such as gaining access to certain electronic device information resources (e.g., through the use of access control policies). Accounting is recording user activity. By way of a summary example, end user devices may be coupled (e.g., through an access network) through an edge ND (supporting AAA processing) coupled to core NDs coupled to electronic devices implementing servers of service/content providers. AAA processing is performed to identify for a subscriber the subscriber record stored in the AAA server for that subscriber. A subscriber record includes a set of attributes (e.g., subscriber name, password, authentication information, access control information, rate-limiting information, policing information) used during processing of that subscriber&#39;s traffic. 
     Certain NDs (e.g., certain edge NDs) internally represent end user devices (or sometimes customer premise equipment (CPE) such as a residential gateway (e.g., a router, modem)) using subscriber circuits. A subscriber circuit uniquely identifies within the ND a subscriber session and typically exists for the lifetime of the session. Thus, a ND typically allocates a subscriber circuit when the subscriber connects to that ND, and correspondingly de-allocates that subscriber circuit when that subscriber disconnects. Each subscriber session represents a distinguishable flow of packets communicated between the ND and an end user device (or sometimes CPE such as a residential gateway or modem) using a protocol, such as the point-to-point protocol over another protocol (PPPoX) (e.g., where X is Ethernet or Asynchronous Transfer Mode (ATM)), Ethernet, 802.1Q Virtual LAN (VLAN), Internet Protocol, or ATM). A subscriber session can be initiated using a variety of mechanisms (e.g., manual provisioning a dynamic host configuration protocol (DHCP), DHCP/client-less internet protocol service (CLIPS) or Media Access Control (MAC) address tracking). For example, the point-to-point protocol (PPP) is commonly used for digital subscriber line (DSL) services and requires installation of a PPP client that enables the subscriber to enter a username and a password, which in turn may be used to select a subscriber record. When DHCP is used (e.g., for cable modem services), a username typically is not provided; but in such situations other information (e.g., information that includes the MAC address of the hardware in the end user device (or CPE)) is provided. The use of DHCP and CLIPS on the ND captures the MAC addresses and uses these addresses to distinguish subscribers and access their subscriber records. 
     A virtual circuit (VC), synonymous with virtual connection and virtual channel, is a connection oriented communication service that is delivered by means of packet mode communication. Virtual circuit communication resembles circuit switching, since both are connection oriented, meaning that in both cases data is delivered in correct order, and signaling overhead is required during a connection establishment phase. Virtual circuits may exist at different layers. For example, at layer 4, a connection oriented transport layer datalink protocol such as Transmission Control Protocol (TCP) may rely on a connectionless packet switching network layer protocol such as IP, where different packets may be routed over different paths, and thus be delivered out of order. Where a reliable virtual circuit is established with TCP on top of the underlying unreliable and connectionless IP protocol, the virtual circuit is identified by the source and destination network socket address pair, i.e. the sender and receiver IP address and port number. However, a virtual circuit is possible since TCP includes segment numbering and reordering on the receiver side to prevent out-of-order delivery. Virtual circuits are also possible at Layer 3 (network layer) and Layer 2 (datalink layer); such virtual circuit protocols are based on connection oriented packet switching, meaning that data is always delivered along the same network path, i.e. through the same NEs/VNEs. In such protocols, the packets are not routed individually and complete addressing information is not provided in the header of each data packet; only a small virtual channel identifier (VCI) is required in each packet; and routing information is transferred to the NEs/VNEs during the connection establishment phase; switching only involves looking up the virtual channel identifier in a table rather than analyzing a complete address. Examples of network layer and datalink layer virtual circuit protocols, where data always is delivered over the same path: X.25, where the VC is identified by a virtual channel identifier (VCI); Frame relay, where the VC is identified by a VCI; Asynchronous Transfer Mode (ATM), where the circuit is identified by a virtual path identifier (VPI) and virtual channel identifier (VCI) pair; General Packet Radio Service (GPRS); and Multiprotocol label switching (MPLS), which can be used for IP over virtual circuits (Each circuit is identified by a label). 
     Certain NDs (e.g., certain edge NDs) use a hierarchy of circuits. The leaf nodes of the hierarchy of circuits are subscriber circuits. The subscriber circuits have parent circuits in the hierarchy that typically represent aggregations of multiple subscriber circuits, and thus the network segments and elements used to provide access network connectivity of those end user devices to the ND. These parent circuits may represent physical or logical aggregations of subscriber circuits (e.g., a virtual local area network (VLAN), a permanent virtual circuit (PVC) (e.g., for Asynchronous Transfer Mode (ATM)), a circuit-group, a channel, a pseudo-wire, a physical NI of the ND, and a link aggregation group). A circuit-group is a virtual construct that allows various sets of circuits to be grouped together for configuration purposes, for example aggregate rate control. A pseudo-wire is an emulation of a layer 2 point-to-point connection-oriented service. A link aggregation group is a virtual construct that merges multiple physical NIs for purposes of bandwidth aggregation and redundancy. Thus, the parent circuits physically or logically encapsulate the subscriber circuits. 
     Each VNE (e.g., a virtual router, a virtual bridge (which may act as a virtual switch instance in a Virtual Private LAN Service (VPLS) is typically independently administrable. For example, in the case of multiple virtual routers, each of the virtual routers may share system resources but is separate from the other virtual routers regarding its management domain, AAA (authentication, authorization, and accounting) name space, IP address, and routing database(s). Multiple VNEs may be employed in an edge ND to provide direct network access and/or different classes of services for subscribers of service and/or content providers. 
     Within certain NDs, “interfaces” that are independent of physical NIs may be configured as part of the VNEs to provide higher-layer protocol and service information (e.g., Layer 3 addressing). The subscriber records in the AAA server identify, in addition to the other subscriber configuration requirements, to which context (e.g., which of the VNEs/NEs) the corresponding subscribers should be bound within the ND. As used herein, a binding forms an association between a physical entity (e.g., physical NI, channel) or a logical entity (e.g., circuit such as a subscriber circuit or logical circuit (a set of one or more subscriber circuits)) and a context&#39;s interface over which network protocols (e.g., routing protocols, bridging protocols) are configured for that context. Subscriber data flows on the physical entity when some higher-layer protocol interface is configured and associated with that physical entity. 
     Some NDs provide support for implementing VPNs (Virtual Private Networks) (e.g., Layer 2 VPNs and/or Layer 3 VPNs). For example, the ND where a provider&#39;s network and a customer&#39;s network are coupled are respectively referred to as PEs (Provider Edge) and CEs (Customer Edge). In a Layer 2 VPN, forwarding typically is performed on the CE(s) on either end of the VPN and traffic is sent across the network (e.g., through one or more PEs coupled by other NDs). Layer 2 circuits are configured between the CEs and PEs (e.g., an Ethernet port, an ATM permanent virtual circuit (PVC), a Frame Relay PVC). In a Layer 3 VPN, routing typically is performed by the PEs. By way of example, an edge ND that supports multiple VNEs may be deployed as a PE; and a VNE may be configured with a VPN protocol, and thus that VNE is referred as a VPN VNE. 
     Some NDs provide support for VPLS (Virtual Private LAN Service). For example, in a VPLS network, end user devices access content/services provided through the VPLS network by coupling to CEs, which are coupled through PEs coupled by other NDs. VPLS networks can be used for implementing triple play network applications (e.g., data applications (e.g., high-speed Internet access), video applications (e.g., television service such as IPTV (Internet Protocol Television), VoD (Video-on-Demand) service), and voice applications (e.g., VoIP (Voice over Internet Protocol) service)), VPN services, etc. VPLS is a type of layer 2 VPN that can be used for multi-point connectivity. VPLS networks also allow end use devices that are coupled with CEs at separate geographical locations to communicate with each other across a Wide Area Network (WAN) as if they were directly attached to each other in a Local Area Network (LAN) (referred to as an emulated LAN). 
     In VPLS networks, each CE typically attaches, possibly through an access network (wired and/or wireless), to a bridge module of a PE via an attachment circuit (e.g., a virtual link or connection between the CE and the PE). The bridge module of the PE attaches to an emulated LAN through an emulated LAN interface. Each bridge module acts as a “Virtual Switch Instance” (VSI) by maintaining a forwarding table that maps MAC addresses to pseudowires and attachment circuits. PEs forward frames (received from CEs) to destinations (e.g., other CEs, other PEs) based on the MAC destination address field included in those frames. 
     While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described, can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting.