PAPMSC: Power-Aware Performance Management Approach for Virtualized Web Servers via Stochastic Control

• Published in: Journal of grid computing Vol. 14; no. 1; pp. 171 - 191
• Main Authors: Shi, Xiaoyu, Dong, Jin, Djouadi, Seddik M., Feng, Yong, Ma, Xiao, Wang, Yefu
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2016
• Subjects: Computer Science; Controllers; Management of Computing and Information Systems; Mathematical models; Optimal control; Processor Architectures; Servers; Servers (computers); Stochastic processes; User Interfaces and Human Computer Interaction; Workload; World Wide Web; Stochastic control; Power efficiency; Resource management; Virtualization; Web application
• ISSN: 1570-7873; 1572-9184
• DOI: 10.1007/s10723-015-9341-z

As green computing is becoming a popular computing paradigm, the performance of energy-efficient data center becomes increasingly important. This paper proposes power-aware performance management via stochastic control method (PAPMSC), a novel stochastic control approach for virtualized web servers. It addresses the instability and inefficiency issues due to dynamic web workloads. It features a coordinated control architecture that optimizes the resource allocation and minimizes the overall power consumption while guaranteeing the service level agreements (SLAs). More specifically, due to the interference effect among the co-located virtualized web servers and time-varying workloads, the relationship between the hardware resource assignment to different virtual servers and the web applications’ performance is considered as a coupled Multi-Input-Multi-Output (MIMO) system and formulated as a robust optimization problem. We propose a constrained stochastic linear-quadratic controller (cSLQC) to solve the problem by minimizing the quadratic cost function subject to constraints on resource allocation and applications’ performance. Furthermore, a proportional controller is integrated to enhance system stability. In the second layer, we dynamically manipulate the physical frequency for power efficiency using an adaptive linear quadratic regulator (ALQR). Experiments on our testbed server with a variety of workload patterns demonstrate that the proposed control solution significantly outperforms existing solutions in terms of effectiveness and robustness.