Cloud workflow segmentation online scheduling optimization method integrating heuristic and intelligent computing

Abstract

The invention discloses a cloud workflow segmented online scheduling optimization method integrating heuristics and intelligent computing, which comprises the following steps: obtaining information required for scheduling optimization; calculating the sorting value and level value of tasks; The heuristic method generates the task scheduling optimization scheme of the first stage; obtains the task scheduling optimization scheme of the second stage based on the genetic algorithm; outputs the scheduling optimization scheme. The present invention adopts a segmented scheduling optimization method that combines heuristics and intelligent computing, and the solution time is equal to that of the heuristic method, while the quality of the solution is similar to that obtained by the intelligent computing method. Therefore, the present invention adapts to real-time online scheduling. Effectively improve the quality of understanding under the premise.

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;