VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

  • Authors:

  • Weiwei Fang;Xiangmin Liang;Shengxin Li;Luca Chiaraviglio;Naixue Xiong

  • Affiliations:

  • School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Department of Electronics, Politecnico di Torino, Turin 10129, Italy;School of Computer Science, Colorado Technical University, Colorado Springs, Colorado 80907, USA

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2013

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Abstract

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.