Optimal robust configuration in cloud environment based on heuristic optimization algorithm

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system's actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10^-6, indicating the accuracy of our solution.