An adaptive power management framework for autonomic resource configuration in cloud computing infrastructures

Abstract

Power is becoming an increasingly important concern for large-scale cloud computing systems. Meanwhile, cloud service providers leverage virtualization technologies to facilitate service consolidation and enhance resource utilization. However, the introduction of virtualization makes the cloud infrastructure more complex, and thus challenges cloud power management. In a virtualized environment, resource needs to be configured at runtime at the cloud, server and virtual machine levels to achieve high power efficiency. In addition, cloud power management should guarantee high users' SLA (service level agreement) satisfaction. In this paper, we present an adaptive power management framework in the cloud to achieve autonomic resource configuration. We propose a software and lightweight approach to accurately estimate the power usage of virtual machines and cloud servers. It explores hypervisor-observable performance metrics to build the power usage model. To configure cloud resources, we consider both the system power usage and the SLA requirements, and leverage learning techniques to achieve autonomic resource allocation and optimal power efficiency. We implement a prototype of the proposed power management system and test it on a cloud testbed. Experimental results show the high accuracy (over 90%) of our power usage estimation mechanism and our resource configuration approach achieves the lowest energy usage among the compared four approaches.