Provision of Data-Intensive Services Through Energy- and QoS-Aware Virtual Machine Placement in National Cloud Data Centers

Abstract

Many data-intensive services (e.g., planet analysis, gene analysis, and so on) are becoming increasingly reliant on national cloud data centers (NCDCs) because of growing scientific collaboration among countries. In NCDCs, tens of thousands of virtual machines (VMs) are assigned to physical servers to provide data-intensive services with a quality-of-service (QoS) guarantee, and consume a massive amount of energy in the process. Although many VM placement schemes have been proposed to solve this problem of energy consumption, most of these assume that all the physical servers are homogeneous. However, the physical server configurations of NCDCs often differ significantly, which leads to varying energy consumption characteristics. In this paper, we explore an alternative VM placement approach to minimize energy consumption during the provision of data-intensive services with a global QoS guarantee in NCDCs. We use an improved particle swarm optimization algorithm to develop an optimal VM placement approach involving a tradeoff between energy consumption and global QoS guarantee for data-intensive services. Experimental results show that our approach significantly outperforms other approaches to energy optimization and global QoS guarantee in NCDCs.