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Application US20200104184
Inventor

Accelerated Resource Allocation Techniques

Examples described herein can be used to determine and suggest a computing resource allocation for a workload request made from an edge gateway. The computing resource allocation can be suggested using computing resources provided by an edge server cluster. Telemetry data and performance indicators of the workload request can be tracked and used to determine the computing resource allocation. Artificial intelligence (AI) and machine learning (ML) techniques can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation. Suggestions made can be accepted or rejected by a resource allocation manager for the edge gateway and the edge server cluster.

Slightly More than Average Length Specification


1 Independent Claims

  • Claim 1. A method comprising: determining whether to allocate computer resources for a workload, wherein: the workload is performed for a client device and the computer resources are part of an edge computing cluster physically proximate to a gateway in communication with the client devicerequesting a recommended recommendation of a computer resource allocationapplying an artificial intelligence (AI) model to determine the recommendation of the computer resource allocation for the workload, the model to determine the computer resource allocation based at least, in part, on measured performance associated with one or more prior performances of at least a portion of the workloaddetermining whether to accept the recommendation of the computer resource allocationand in response to a determination to accept the recommendation of the computer resource allocation: causing at least a portion of the edge computing cluster to perform the workload using the recommendation of the computer resource allocation, determining at least one performance indicator associated with the performance of the workload, and providing a reward based at least, in part, on the at least one performance indicator.
  • Claim 9. An apparatus comprising: a resource manager for an edge computing cluster and an interface capable to communicatively couple with the edge computing cluster when connected with the edge computing cluster, wherein the resource manager comprises: at least one processorat least one memory communicatively coupled to the at least one processor, wherein the at least one processor is to: identify a resource allocation scenario for a workload Aapply an artificial intelligence (AI) model to determine a resource allocation recommendation for the workload A, the AI model trained based on rewards arising out of resource allocation recommendations made for one or more workloads that are substantially similar to workload Adetermine whether to accept the resource allocation recommendation for the workload Ain response to a determination to accept the resource allocation recommendation: cause the edge computing cluster, when coupled to the interface, to use the resource allocation recommendation to perform workload Aand determine a reward associated with the resource allocation recommendation for workload A to be provided to the AI model.
  • Claim 19. A computer-readable medium comprising instructions, that if executed by one or more machines, cause the one or more machines to: identify a resource allocation scenario for a workload Aapply an artificial intelligence (AI) model to determine a resource allocation recommendation for the workload A, the AI model trained based on rewards arising out of resource allocation recommendations made for one or more workloads that are substantially similar to workload Adetermine whether to accept the resource allocation recommendation for the workload Ain response to a determination to accept the resource allocation recommendation: cause the edge computing cluster to use the resource allocation recommendation to perform workload Aand determine a reward associated with the resource allocation recommendation for workload A to be provided to the AI model.


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