IMMUNE MULTI-OBJECTIVE OPTIMIZATION SCHEDULING ALGORITHM FOR CLOUD PLATFORM
In the cloud environment,there are various characteristics among tasks.Due to the changes and uncertainties in the traditional resource allocation mechanism,load imbalance is easy to cause scheduling constraints,and task delay constraints also reduce the utilization of task scheduling policies.To solve this problem,a cloud-oriented platform immune multi-objective optimization scheduling algorithm is proposed.Pareto dominance relation was used to design the mathematical model of cloud computing task scheduling problem.After population initialization,Pareto optimal solution,calculation of crowding distance,clone selection,recombination and variation,the diversity of population was maintained and the global optimization of scheduling was realized.Compared with the traditional algorithm,the experiments show that the proposed algorithm has a wider search range,better search breadth of solutions,and can balance the task execution time and cost effectively,and improve user satisfaction.
国家科技期刊平台
NSTL
万方数据
维普
