Workflow scheduling optimization method based on multi-stage genetic algorithm in cloud computing environment

Abstract

The invention discloses a workflow scheduling optimization method based on a multi-stage genetic algorithm in a cloud computing environment. The workflow scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization, calculating a task hierarchy value, and initializing a contemporary population and elite individuals; carrying out adaptive evolution in three stages, wherein the heuristic method based on the earliest task completion time in the first stage and the second stage and the task scheduling sequence list genetic operation basedon hierarchy and topological sorting enable the algorithm to converge near the optimal solution as soon as possible, and the genetic operation of a virtual machine allocation list and the task scheduling sequence list is adopted for neighborhood expansion search in the third stage; and meanwhile, a serial decoding method based on an insertion mode, an elitist saving mechanism and an FBI & D and LDI improvement strategy are adopted in evolution. Compared with a single-stage search algorithm, the method has better search efficiency and optimization capability.

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 workflow scheduling optimization method based on a multi-stage genetic algorithm in a cloud computing environment. 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 the...

AI Technical Summary

Problems solved by technology

[0012] In order to overcome that the quality of the heuristic method solution is usually not very high and depends on the type of workflow, the semi-intelligent computing method combined with heuristics and the intelligent computing method based on layered coding have incompleteness in the search space. It will lead to a decrease in search efficiency and low efficiency of single-stage evolution. The present invention provides a workflow scheduling optimization method based on a multi-stage genetic algorithm in a cloud computing environment, which effectively improves the efficiency and quality of the solution.

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