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Patent US0200104184
Inventor

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Slightly More than Average Length Specification


1 Independent Claims

  • Claim 2. The method of claim 1, wherein the determining whether to allocate computer resources for a workload comprises determining whether to allocate computer resources for a workload based at least in part on one or more of: receipt of one or more of a workload request from a network gateway or client device, an update to hardware or software used by the edge computing resource, a change in service level agreement (SLA) or quality of service (QoS) requirements, or an imminent change in life stage of an application or process.
  • Claim 3. The method of claim 1, wherein the applying an artificial intelligence (AI) model comprises using any or a combination of: a reinforcement learning model, a Q-learning model, a deep-Q learning model, an Asynchronous Advantage Actor-Critic (A3C) model, combinatorial neural network, or recurrent combinatorial neural network.
  • Claim 4. The method of claim 1, wherein the determining whether to accept the recommendation of the computer resource allocation is based at least, in part, on one or more of: use of the recommendation of the computer resource allocation will not yield compliance with a service level agreement (SLA) or the AI model is early in its training phase.
  • Claim 5. The method of claim 1, further comprising: receiving telemetry data using out of band (OOB) channels, the telemetry data arising out of the performance of the workload, wherein the determining at least one performance indicator associated with the performance of the workload is based at least, in part, on the received telemetry data.
  • Claim 6. The method of claim 1, wherein providing a reward based at least, in part, on the at least one performance indicator comprises providing a reward based at least, in part, on one or more of: boundedness, end-to-end latency, computer resource utilization, meeting or failing performance requirements, meeting or failing quality of service (QoS) requirements, conflicting requirements of other workloads executing simultaneously on at least portions of the same resources, or rejection of a resource configuration suggestion.
  • Claim 7. The method of claim 1, further comprising: storing information related to a performance of the workload, wherein the information comprises one or more of: a workload identifier, accumulated reward, boundedness measurement, performance measurement, and resource allocation.
  • Claim 8. The method of claim 1, further comprising: receiving a request to perform a second workloaddetermining whether at least a portion of the second workload has been performed before using the edge computing clusterrequesting a recommendation of the second computer resource allocationdetermining whether the second workload is substantially the same as the workloadand in response to a determination that the second workload is substantially the same as the workload: applying the AI model to determine the recommendation of the second computer resource allocation for the second workload, the AI model to determine the second computer resource allocation based at least, in part, on measured performance associated with the second workloadcausing the edge computing cluster to perform the second workload using the recommendation of the second computer resource allocationdetermining at least one performance indicator associated with the performance of the second workloadand providing a second reward for the second workload based at least, in part, on the at least one performance indicator, wherein: the at least one performance indicator is based at least, in part, on one or more of: boundedness, end-to-end latency, computer resource utilization, meeting or failing performance requirements, or rejection of a resource configuration suggestionand accumulating the second reward with an accumulated award for the workload.
  • 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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