Cloud workflow scheduling optimization method adopting multi-population coevolution genetic algorithm

Abstract

The invention discloses a cloud workflow scheduling optimization method adopting a multi-population coevolution genetic algorithm. The method comprises the following steps: acquiring information required by scheduling optimization; calculating a sorting value rank of the tasks; initializing a population based on key task priority scheduling, the earliest task completion time and a random generation method; performing independent evolution on a plurality of sub-populations, and timely performing communication among the sub-populations; and until a termination condition is satisfied, outputtinga scheduling optimization scheme. According to the method, an integer coding method based on topological sorting, a serial individual decoding method based on an insertion mode, an improved strategy of forward and backward decoding and load balancing and a multi-population coordinated evolution mechanism are designed and adopted, so that each sub-population can be effectively prevented from entering local optimum and premature, the optimization capability is improved, and the overall efficiency of the algorithm is improved.

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 scheduling optimization method using a multi-population co-evolutionary genetic algorithm. 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 tasks in...

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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, combined with the heuristic semi-intelligent computing method and the intelligent computing method based on hierarchical coding to encode the incompleteness of the search space, based on the global single population intelligence The calculation method has low search efficiency and other shortcomings. The present invention provides a cloud workflow scheduling optimization method using a multi-population co-evolutionary genetic algorithm, which effectively improves the efficiency and quality of the solution.

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