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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 695757, 18 pages
Research Article

Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

System Simulation Lab, Mechatronics and Atuomation School, National University of Defense Technology, Hunan Province, Changsha, 410073, China

Received 27 February 2012; Revised 26 June 2012; Accepted 5 July 2012

Academic Editor: Rubén Ruiz García

Copyright © 2012 Xiaocheng Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment.