| Popis: |
As a novel computing paradigm,edge computing has become a significant approach to solve large-scale scientific applications.Aiming at scientific workflow scheduling under edge environment,task computation time and data transmission time are uncertain due to the fluctuation of server processing performance and bandwidth,respectively.In order to help capture and reflect the uncertainty during workflow execution,task computation time and data transmission time are represented as triangular fuzzy numbers (TFN),based on fuzzy theory.Simultaneously,an adaptive discrete fuzzy GA-based particle swarm optimization (ADFGA-PSO) is proposed to minimize fuzzy execution cost of workflow while satisfying deadline constraint.Besides,two-point crossover operator,neighborhood mutation and adaptive multipoint mutation operator of genetic algorithm (GA) are introduced to avoid particles being trapped in local optimum.Experimental results show that,compared with others,scheduling strategy based on ADFGA-PSO can more effectively reduce fuzzy execution cost in regard to deadline-constrained scientific workflow scheduling under edge environment. |
