A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

Article English OPEN
Lin, Weiwei ; Wu, Wentai ; Wang, James Z. (2016)
  • Publisher: Hindawi Publishing Corporation
  • Journal: Scientific Programming (issn: 1058-9244, eissn: 1875-919X)
  • Related identifiers: doi: 10.1155/2016/7040276
  • Subject: Computer software | Article Subject | QA76.75-76.765

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.
  • References (27)
    27 references, page 1 of 3

    Pedram, M.. Energy-efficient datacenters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2012; 31 (10): 1465-1484

    Koomey, J.. Growth in data center electricity use 2005 to 2010. A Report by Analytical Press. 2011

    Sampaio, A. M., Barbosa, J. G.. Towards high-available and energy-efficient virtual computing environments in the cloud. Future Generation Computer Systems. 2014; 40: 30-43

    Deng, Z., Zeng, G., He, Q., Zhong, Y., Wang, W.. Using priced timed automaton to analyse the energy consumption in cloud computing environment. Cluster Computing. 2014; 17 (4): 1295-1307

    Tian, Y., Lin, C., Li, K.. Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing. Cluster Computing. 2014; 17 (3): 943-955

    Lee, H. M., Jeong, Y.-S., Jang, H. J.. Performance analysis based resource allocation for green cloud computing. The Journal of Supercomputing. 2014; 69 (3): 1013-1026

    Horri, A., Mozafari, M. S., Dastghaibyfard, G.. Novel resource allocation algorithms to performance and energy efficiency in cloud computing. Journal of Supercomputing. 2014; 69 (3): 1445-1461

    Maheswaran, M., Ali, S., Siegel, H. J., Hensgen, D., Freund, R. F.. Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. Journal of Parallel & Distributed Computing. 1999; 59 (2): 107-131

    Priya, S. M., Subramani, B.. A new approach for load balancing cloud computing. International Journal of Engineering and Computer Science. 2013; 2 (5): 1636-1640

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