基于多目标免疫系统算法的云任务调度策略

针对云计算环境下任务调度问题,为减少任务完工时间,同时降低任务执行费用,提出一种改进的基于多目标免疫系统的任务调度算法IMISA来寻找较优的可行分配方案。与传统分配适应度值不同,该算法将抗体群划分为非支配解集和支配解集,分别将非支配解的独立支配区域面积、支配解与所有非支配解所围成的多边形面积作为相应的抗体-抗原亲和力,根据相应亲和度计算克隆比例后克隆变异生成子代。在CloudSim平台上进行仿真实验,结果表明,与NSGA-Ⅱ及多目标免疫系统算法(MISA)相比,IMISA能够找到具有更短完工时间及更小的执行费用的调度方案,同时获得的Pareto解集也具有更好的分布性。

Multi-objective immune system algorithm for task scheduling in cloud computing

To address the task scheduling problem in cloud, a new multi-objective immune system algorithm named IMISA (Improved Multi-objective Immune System Algorithm) was proposed to optimize completion time and monetary cost simultaneously. In this work, the assignment of the fitness value was different from the traditional way, the antibodies were divided into non-dominated antibodies and dominated antibodies. The exclusive dominated area was regarded as the antigen affinity, and the polygonal area surrounded by all the non-dominated solutions and a selection of the dominated antibody were treated as the corresponding antibody-antigen affinity. After that, clonal probability was calculated according to these affinities and new offspring was generated by cloning and mutation. Simulation experiments on CloudSim indicate that, compared with NSGA-Ⅱ and Multi-objective Immune System Algorithm (MISA), the proposed algorithm can produce schemes with shorter completion time and lower monetary cost, and it also can achieve Pareto fronts with better quality in both convergence and diversity.