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System and method for applying machine learning algorithms to compute health scores for workload scheduling

A healthy and fractional technology, applied in digital transmission systems, computing, transmission systems, etc., can solve problems such as suboptimal scheduling decisions, inaccurate health assessments, and weakening algorithms for efficient scheduling.

Pending Publication Date: 2020-03-06
CISCO TECH INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Current network health algorithms use static weights and other static coefficients in their health calculations, which can produce less than accurate health assessments
This inaccuracy impairs the algorithm's ability to schedule workloads as efficiently as possible and leads to suboptimal scheduling decisions

Method used

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  • System and method for applying machine learning algorithms to compute health scores for workload scheduling
  • System and method for applying machine learning algorithms to compute health scores for workload scheduling
  • System and method for applying machine learning algorithms to compute health scores for workload scheduling

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Embodiment Construction

[0017] The present disclosure addresses the issues raised above. The present disclosure provides examples of systems, methods, and computer-readable storage devices. First, the figure 1 A general example system is disclosed in that may provide some of the basic hardware components that make up a server, node, or other computer system.

[0018] figure 1 Computing system architecture 100 is shown in which components of the system are in electrical communication with each other using connectors 105 . Exemplary system 100 includes a processing unit (CPU or processor) 110 and a system connector 105, which will include system memory 115 such as read only memory (ROM) 120 and random access memory (RAM) 125 Various system components are coupled to processor 110 . System 100 may include cache memory directly coupled to processor 110 , in close proximity to processor 110 , or integrated as part of processor 110 . System 100 may copy data from memory 115 and / or storage device 130 to...

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Abstract

Disclosed is a method that includes collecting first temporal statistics for a port element in a computing environment, collecting second temporal statistics for a switch element in the computing environment, collecting third temporal statistics for the computing environment generally, computing a spatial correlation between network features and network elements comprising the port element and theswitch element and computing, via a machine learning technique, a port dynamic weight for the port element and a switch dynamic weight for the switch element. The method can also include scheduling workload to consume compute resources within the compute environment based at least in part on the port dynamic weight for the port element and the switch dynamic weight for the switch element.

Description

technical field [0001] The present disclosure provides a method for scheduling workloads in a computing environment by considering spatial characteristics and machine learning techniques for computing health scores of network elements such as switches or ports. This concept enables more accurate scheduling of workloads in a more stable manner based on health scores. Background technique [0002] The prevalence of server virtualization in computing environments, which can include storage virtualization, has led to a rapid growth of cloud-based data centers. In the data center, workloads (virtual machines, containers, or bare metal) are deployed by compute and storage orchestrators based on various heuristics. Typically, these heuristics include server resources such as vCPU, memory usage, and the like. Such heuristics are also often based only on the immediate health of the compute server. Other heuristics include deploying workloads based on application affinity, selectin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50H04L29/08
CPCG06F9/505H04L67/1008H04L67/1012H04L67/101G06F9/45533G06N3/02H04L12/4641H04L43/04
Inventor 奇拉格·塔亚尔埃莎·德赛帕杜·克里希南
Owner CISCO TECH INC
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