Aiming at analyzing performance in cloud computing, some unpredictable perturbations which may lead to performance downgrade are essential factors that should not be neglected. To avoid performance downgrade in cloud computing system, it is reasonable to measure the impact of the perturbations, and further propose a robust scheduling strategy to maintain the performance of the system at an acceptable level. In this paper, we first describe the supply-demand relationship of service between cloud service providers and customers, in which the profit and waiting time are objectives they most concerned. Then, on the basis of introducing the lowest acceptable profit and longest acceptable waiting time for cloud service providers and customers respectively, we define a robustness metric method to declare that the number and speed of servers should be adequately configured in a feasible region, such that the performance of cloud computing system can stay at an acceptable level when it is subject to the perturbations. Subsequently, we discuss the robustness metric method in several cases, and propose heuristic optimization algorithm to enhance the robustness of the system as much as possible. At last, the performances of the proposed algorithm are validated by comparing with DE and PSO algorithm, the results show the superiority of the proposed algorithm.

翻译：针对云计算性能分析中不可忽视的性能降级问题，本文首先探讨了若干不可预测扰动因素对系统性能的影响。为避免云计算系统性能降级，合理评估扰动影响并提出鲁棒调度策略以维持系统性能在可接受水平具有重要意义。本文首先描述了云服务提供商与客户之间的服务供需关系，其中利润和等待时间是双方最关注的目标。随后，在分别引入云服务提供商可接受的最低利润和客户可接受的最长等待时间的基础上，定义了一种鲁棒性度量方法，表明服务器数量和速度应在可行区域内进行充分配置，使得云计算系统在遭受扰动时仍能保持可接受的性能水平。进而，我们通过多个案例讨论了该鲁棒性度量方法，并提出启发式优化算法以最大限度增强系统鲁棒性。最后，通过与DE和PSO算法的对比实验验证了所提算法的性能，结果表明该算法具有优越性。