基于聚类和改进共生演算法的云任务调度策略

针对云计算环境中一些基于服务质量（QoS）调度算法存在寻优速度慢、调度成本与用户满意度不均衡的问题，提出了一种基于聚类和改进共生演算法的云任务调度策略。首先将任务和资源进行模糊聚类并对资源进行重排序放置，依据属性相似度对任务进行指导分配，减小对资源的选择范围；然后依据交叉和旋转学习机制改进共生演算法，提升算法的搜索能力；最后通过加权求和方式构造驱动模型，均衡调度代价与系统性能间关系。通过不同任务量的云任务调度仿真实验，表明该算法相比改进遗传算法、混合粒子群遗传算法和离散共生演算法，有效减少了进化代数，降低了调度成本并提升了用户满意度，是一种可行有效的任务调度算法。

Cloud task scheduling strategy based on clustering and improved symbiotic organisms search algorithm

To solve the problems of some Quality of Service (QoS)-based scheduling algorithms in cloud computing environment, such as slow optimizing speed and imbalance between scheduling cost and user satisfaction, a cloud task scheduling strategy based on clustering and improved SOS (Symbiotic Organisms Search) algorithm was proposed. Firstly, the tasks and resources were clustered by fuzzy clustering and the resources were reordered and placed, and then the tasks were guided and assigned according to the similarity of attributes to reduce the selection range of resources. Secondly, the SOS algorithm was improved according to the cross and rotation learning mechanism to improve the algorithm search ability. Finally, the driving model was constructed by weighted summation to balance the relationship between scheduling cost and system performance. Compared with the improved global genetic algorithm, hybrid particle swarm optimization and genetic algorithm, and discrete SOS algorithm, the proposed algorithm can effectively reduce the evolution generation, reduce the scheduling cost and improve the user's satisfaction. Experimental results show that the proposed algorithm is a feasible and effective task scheduling algorithm.