Heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method

Abstract

The invention discloses a heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method. The heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization; calculating a sorting value and a hierarchical value of the task; generating a first-stage task scheduling optimization scheme based on a heuristic method of dynamic key task priority scheduling; obtaining a task scheduling optimization scheme of a second stage based on a genetic algorithm; and outputting the scheduling optimization scheme. According to the heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method, thesegmented scheduling optimization method integrating the heuristic method and the intelligent calculation method is adopted, and the solving time is equal to the solving time of the heuristic method,and the quality of the solution is similar to the quality of the solution solved by the intelligent calculation method, so that the understanding quality is effectively improved on the premise of adapting to real-time online scheduling.

Description

technical field [0001] The present invention relates to the fields of computer technology, information technology and system engineering, in particular to a cloud workflow scheduling optimization method, more specifically, to a cloud workflow segmented online scheduling optimization method that integrates heuristics and intelligent computing. Background technique [0002] Workflow under the cloud computing environment, referred to as "cloud workflow", is the integration of cloud computing and workflow-related technologies, and has a wide range of applications in cross-organizational business collaboration and scientific computing that require efficient computing performance and large-scale storage support. prospect. In cloud workflow, there are timing constraints between tasks, and virtual machines are usually used as the smallest allocation unit of computing resources to receive and process these tasks during execution. Cloud workflow scheduling refers to how to allocate t...

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Problems solved by technology

[0009] 2) The algorithmic efficiency of intelligent computing methods depends on encoding and decoding, the design of evolutionary iterative strategies, and the selection of control parameters. Among them, the solution space searched by combined heuristic semi-intelligent computing methods, that is, the scheduling scheme, is incomplete, so its Theoretically, there is a possibility that the optimal scheduling scheme cannot be searched, and at the same time, the heuristic method needs to be called continuously in the algorithm, and its time efficiency is not very high; the intelligent computing method can theoretically realize the global search, but using the global search will lead to The search efficiency is reduced; in general, the solution quality of the intelligent computing method is better than that of the heuristic method, but its solution time is higher than that of the heuristic method, which is not suitable for online real-time scheduling optimization;

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