Achieving Fair Share Objectives via Goal-Oriented Parallel Computer Job Scheduling Policies
Authors: Sangsuree Vasupongayya
Fair share is one of the scheduling objectives supported on many production systems. However, fair share has been shown to cause performance problems for some users, especially the users with difficult jobs. This work is focusing on extending goaloriented parallel computer job scheduling policies to cover the fair share objective. Goal-oriented parallel computer job scheduling policies have been shown to achieve good scheduling performances when conflicting objectives are required. Goal-oriented policies achieve such good performance by using anytime combinatorial search techniques to find a good compromised schedule within a time limit. The experimental results show that the proposed goal-oriented parallel computer job scheduling policy (namely Tradeofffs( Tw:avgX)) achieves good scheduling performances and also provides good fair share performance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070925Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 908
 OpenPBS, http://www.nas.nasa.gov/Software/PBS/
 PBS pro, http://www.pbspro.com
 LSF, http://www.platform.com/product/ lsffamily.
 LSF fair share documentation, http://accl.grc.nasa.gov/ job_schedulers/lsf/ Docs/lsf6.1/lsf6.1_admin /E_fairshare.html
 D. Jackson, Q. Snell & M. Clement. "Core algorithms of the MAUI scheduler". In proceeding of the Workshop on Job Scheduling Strategies for Parallel Processing, 2001.
 Maui scheduler, http://www.supercluster.org/maui
 Moab scheduler, http://www.clusterresources.com/products/mwm/ docs/moabadminguide450.pdf
 S. Kannan, M. Roberts, P. Mayes, D. Brelsford & J. Skovira. "Workload management with LoadLeveler". Technical Report, IBM Redbook, 2001.
 J. Key & P. Lauder. "A fair share scheduler". Communications of the ACM, 31(3):44-55, 1988.
 S. Vasupongayya, "Impact of fair share and its configurations on parallel job scheduling algorithms". (to appear). In proceeding of the 2009 WASET International Conference on High Performance Computing, Venice, Italy, October 2009.
 S.-H. Chiang and S. Vasupongayya, "Design and potential performance of goal-oriented job scheduling policies for parallel computer workloads". In the IEEE Transaction on Parallel and Distributed Systems. 19(12):1642-1656, 2009.
 S. Vasupongayya, "Goal-oriented parallel job scheduling: A revisit", In proceeding of the 2nd UBU-Research, Ubonratchathani, Thailand, July 2008.
 S. Vasupongayya, S.-H Chiang and B. Massey, "Search-based job scheduling for parallel computer workloads", In proceeding of the IEEE Cluster, Boston, MA, 2005.
 S. Kleban and S. Clearwater. "Fair share on high performance computing system: What does fair really mean?" in proceeding of the IEEE International Symposium on Cluster Computing and the Grid, 2003.
 S. Vasupongayya and S.-H. Chiang. "Multi-objective models for scheduling jobs on parallel computer systems". In proceeding of IEEE Cluster, Barcelona, Spain, 2006.
 S.-H. Chiang, A. Arpaci-Dusseau and M. Vernon. "The impact of more accurate request runtimes on production job scheduling performance". In Lecture Notes in Computer Science (2537):103-127, 2002.
 S.-H. Chiang and C. Fu. "Benefit of limited time-sharing in the presence of very large parallel jobs". In proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2005.
 S.-H. Chiang and M. Vernon. "Production job scheduling for parallel shared memory systems". In proceeding of the IEEE International Parallel and Distributed Processing Symposium, 2001.
 D. Talby and D. Feitelson, "Supporting priorities and improving utilization of the IBM SP2 scheduler using slack-based backfilling". In proceeding of the International Parallel Processing Symposium, 1999.
 D. Talby and D. Feitelson, "Improving and stabilizing parallel computer performance using adaptive backfilling". In proceeding of the IEEE International Parallel and Distributed Processing Symposium, 2005.