›› 2013, Vol. 28 ›› Issue (6): 948-961.doi: 10.1007/s11390-013-1390-9

Special Issue: Data Management and Data Mining

• Special Section on Cloud Data Management • Previous Articles     Next Articles

An Energy-Aware Heuristic Scheduling for Data-Intensive Workflows in Virtualized Datacenters

Peng Xiao1, 2 (肖鹏), Student Member, CCF, ACM, IEEE, Zhi-Gang Hu2, * (胡志刚), Senior Member, CCF, and Yan-Ping Zhang2, 3(张艳平), Student Member, ACM, IEEE   

  1. 1 School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;
    2 School of Information Science and Engineering, Central South University, Changsha 410083, China;
    3 College of Computation and Bioinformatics, Technical University of Munich, Freising 85354, Germany
  • Received:2012-09-02 Revised:2013-05-06 Online:2013-11-05 Published:2013-11-05
  • About author:Peng Xiao received the M.S degree in computer science from Xiamen University, China. Now, he is currently a Ph.D. candidate in Central South University, Changsha and a lecturer in Hunan Institute of Engineering, Xiangtan. His research interests include cloud computing, resource virtualization technology, and HPC energy-efficiency management.
    Zhi-Gang Hu received his M.S. and Ph.D. degrees in computer science from Central South University, Changsha. Now he is a professor in Central South University. His current research interests include parallel and distributed computing, cloud computing, grid computing, green HPC policy and management, and power optimization in embedded systems.
    Yan-Ping Zhang received her M.S. degree in computer science from Central South University, Changsha. Now she is a Ph.D. candidate in Technical University of Munich, Germany. Her research interests include cloud-based large-scale application, virtual machine power model, workflow scheduling model and algorithm.
  • Supported by:

    Supported by the National Natural Science Foundation of China under Grant Nos. 60970038, 61272148, the Science and Technology Plan Project of Hunan Province of China under Grant No. 2012GK3075, and the Scientific Research Fund of Hunan Provincial Education Department of China under Grant No. 13B015.

With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an energyaware scheduling algorithm is proposed, which introduces a novel heuristic called Minimal Data-Accessing Energy Path for scheduling data-intensive workflows aiming to reduce the energy consumption of intensive data accessing. Extensive experiments based on both synthetical and real workloads are conducted to investigate the effectiveness and performance of the proposed scheduling approach. The experimental results show that the proposed heuristic scheduling can significantly reduce the energy consumption of storing/retrieving intermediate data generated during the execution of data-intensive workflow. In addition, it exhibits better robustness than existing algorithms when cloud systems are in presence of I/O-intensive workloads.

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