Robust Scheduling in Cloud Environment Based on Heuristic Optimization Algorithm

TL;DR

This paper proposes a heuristic optimization algorithm to improve the robustness of cloud computing scheduling against performance perturbations, ensuring acceptable profit and waiting times under various scenarios.

Contribution

It introduces a robustness metric for cloud scheduling and develops a heuristic algorithm that outperforms DE and PSO in maintaining system performance.

Findings

The proposed algorithm enhances system robustness under perturbations.

It outperforms DE and PSO algorithms in experiments.

The robustness metric effectively guides server configuration.

Abstract

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.