| Summary: | As more and more computationally intensive dependent applications are offloaded to the cloud environment for execution,the problem of workflow scheduling has received extensive attention.Aiming at the workflow scheduling problem of multi-objective optimization in cloud environment,and considering that the server may experience performance fluctuations and downtime during task execution,based on fuzzy theory,a triangular fuzzy number is used to represent task execution time and data transmission time.A genetic algorithm-based adaptive particle swarm optimization based GA(APSOGA) is proposed.The purpose is to comprehensively optimize the completion time and execution cost of the workflow under the reliability constraints of the workflow.In order to avoid the premature convergence problem of the traditional particle swarm optimization algorithm,the proposed algorithm introduces the random two-point crossover operation and single-point mutation operation of the genetic algorithm,which effectively improves the search performance of the algorithm.Experimental results show that,compared with other strategies,APSOGA-based scheduling strategy can effectively reduce the time and cost of reliability-constrained scientific workflows in cloud environments.
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