Optimization and stabilization of composite service processing in a cloud system

With virtual machines (VM), we design a cloud system aiming to optimize the overall performance, in processing user requests made up of composite services. We address three contributions. (1) We optimize VM resource allocation with a minimized processing overhead subject to task's payment budget. (2) For maximizing the fairness of treatment in a competitive situation, we investigate the best-suited scheduling policy. (3) We devise a resource sharing scheme adjusted based on Proportional-Share model, further mitigating the resource contention. Experiments confirm two points: (1) mean task response time approaches the theoretically optimal value in non-competitive situation; (2) as system runs in short supply, each request could still be processed efficiently as compared to their ideal results. Combining Lightest Workload First (LWF) policy with Adjusted Proportional-Share Model (LWF+APSM) exhibits the best performance. It outperforms others in a competitive situation, by 38% w.r.t. worst-case response time and by 12% w.r.t. fairness of treatment.