Simulation of Task Scheduling Method with Low Consumption under Cloud Computing Environment

A task scheduling method based on improved immune evolutionary algorithm is proposed in the paper.The general immune evolutionary method was analyzed,the PSO algorithm was taken as an operator and to be introduced into the immune evolutionary algorithm. The immune algorithm was improved,and the task scheduling optimization model was provided. The initial antibody was randomly formed in the solution space,and the population size,mutation probability and the number of generation of maximum iteration were initialized. The affinity of each antibody in antibody population was solved and arranged. Certain antibody was mutated,to form new antibody population. The affinity was calculated and arranged again,and the best antibody was selected from the new antibody population. By using PSO operator,the new antibodies were processed,to acquire a set of improved antibody and make statistics of antibodies with larger affinity. The simulation results show that the proposed method used in cloud computing scheduling has high efficiency.