›› 2010, Vol. 25 ›› Issue (4): 864-873.doi: 10.1007/s11390-010-1067-6

• Architecture and High Performance Computer Systems • Previous Articles     Next Articles

Queue Waiting Time Aware Dynamic Workflow Scheduling in Multicluster Environments

Zhi-Feng Yu(余志峰) and Wei-Song Shi(施巍松), Senior Member, IEEE   

  1. Department of Computer Science, Wayne State University, Detroit, MI 48202, U.S.A.
  • Received:2009-08-01 Revised:2010-03-17 Online:2010-07-09 Published:2010-07-09
  • About author:
    Zhi-Feng Yu received the B.S. degree in applied mathematics and M.S. degree in economics and management from Tongji University, Shanghai, China, in 1990 and 1993 respectively and the Ph.D. degree in computer science from Wayne State University, Detroit, USA in 2009. He is currently Manager of Application Development and Integration of TSystems North America, Rochester Hills, MI. His research interests include computer systems and enterprise computing.
    Wei-Song Shi is an associate professor of computer science at Wayne State University. He received his B.S. degree from Xidian University in 1995, and Ph.D. degree from the Chinese Academy of Sciences in 2000, both in computer engineering. His current research focuses on mobile computing, distributed systems and high performance computing. Dr. Shi has published more than 80 peer-reviewed journal and conference papers in these areas. He is the author of the book “Performance Optimization of Software Distributed Shared Memory Systems” (Higher Education Press, 2004). He has also served on technical program committees of several international conferences, including WWW, ICPP, MASS. He is a recipient of Microsoft Fellowship in 1999, the President Outstanding Award of the Chinese Academy of Sciences in 2000, one of 100 Outstanding Ph.D. Dissertations (China) in 2002, “Faculty Research Award” of Wayne State University in 2004 and 2005, the “Best Paper Award” of ICWE’04 and IPDPS’05. He is a recipient of the NSF CAREER award andWayne State University Career Development Chair award.
  • Supported by:

    This work is in part supported by the US National Science Foundation CAREER Grant No. CCF-0643521.

Workflows are prevailing in scientific computation. Multicluster environments emerge and provide more resources, benefiting workflows but also challenging the traditional workflow scheduling heuristics. In a multicluster environment, each cluster has its own independent workload management system. Jobs are queued up before getting executed, they experience different resource availability and wait time if dispatched to different clusters. However, existing scheduling heuristics neither consider the queue wait time nor balance the performance gain with data movement cost. The proposed algorithm leverages the advancement of queue wait time prediction techniques and empirically studies if the tunability of resource requirements helps scheduling. The extensive experiment with both real workload traces and test bench shows that the queue wait time aware algorithm improves workflow performance by 3 to 10 times in terms of average makespan with relatively very low cost of data movement.

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