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, comprising the following steps: acquiring information required for scheduling optimization, calculating task level values, initializing contemporary populations and elite individuals; Adaptive evolution: Phase 1 and Phase 2 based on the heuristic method of the earliest completion time of the task and the task scheduling order list based on hierarchical and topological sorting Genetic operations make the algorithm converge to the vicinity of the optimal solution as soon as possible, while Phase 3 adopts the virtual machine allocation list Neighborhood expansion search is carried out with the genetic operation of task scheduling order list; meanwhile, serial decoding method based on insertion mode, elite preservation mechanism, FBI&D and LDI improvement strategies are adopted in evolution. Compared with the single-stage search algorithm, the present invention has better search efficiency and optimization capability.

Description

technical field [0001] The 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 in cloud computing environment, referred to as "cloud workflow", is the integration of cloud computing and workflow-related technologies. It has a wide range of applications in the fields of cross-organization 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 minimum allocation unit of computing resources to receive and process these tasks during execution. Cloud workflow scheduling refers to how to assign tasks in c...

AI Technical Summary

Problems solved by technology

[0011] 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.

Images