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Coscheduling techniques and monitoring tools for non-dedicated cluster computing
Keywords:
coscheduling, monitoring tool, PVM, Linux, distributed and cluster systemsAbstract
Our efforts are directed towards the understanding of the coscheduling mechanism in a NOW system when a parallel job is executed jointly with local workloads, balancing parallel perfor-mance against the local interactive response. Explicit and implicit coscheduling techniques in a PVM-Linux NOW (or cluster) have been implemented. Furthermore, dynamic coscheduling remains an open question when parallel jobs are executed in a non-dedicated Cluster. A basis model for dynamic coscheduling in Cluster systems is presented in this paper. Also, one dynamic coscheduling algorithm for this model is proposed. The applicability of this algorithm has been proved and its performance ana-lyzed by simulation. Finally, a new tool (named Monito) for monitoring the different queues of messages in such an environments is presented. The main aim of implementing this facility is to provide a mean of capturing the bottlenecks and overheads of the communication system in a PVM-Linux cluster.
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References
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[3] Arpaci-Dusseau, A.C., Culler, D.E. and Mainwaring, A.M.: Scheduling with Implicit Information in Distributed Systems. In Proceedings of the ACM SIGMET-RICS’98/PERFORMANCE’98. 1998.
[4] Dusseau, A.C., Arpaci, R. H. and Culler, D. E.: Effective Distributed Scheduling of Parallel Workloads. In Proceedings of the ACM SIGMETRICS’96. 1996.
[5] Sobalvarro, P.G., Weihl, W.E..: Demand-based Coscheduling of Parallel Jobs on Multiprogrammed Multiprocessors. In Proceedings of the IPPS’95 Workshop on Job Scheduling Strategies for Parallel Processing, pp. 63–75. 1995.
[6] Sobalvarro, P.G., Pakin, S., Weihl, W. E., Chien, A. A.: Dynamic Coscheduling on Workstation Clusters. In Proceedings of the IPPS’98 Workshop on Job Scheduling Strategies for Parallel Processing. 1998.
[7] Wong, Frederick C., Arpaci-Dusseau, Andrea C., Culler, David E.: Building MPI for multi-programming systems using implicit information. In 6th European PVM/MPI User’s Group Meeting. LNCS, Springer, pp. 215–222. 1999.
[8] Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R. and Sunderam, V.: PVM: Parallel Virtual Machine - A User’s Guide and Tutorial for Networked Parallel Computing. MIT Press. 1994.
[9] Message Passing Interface Forum: MPI: A Message-Passing Interface Standard. www.unix.mcs.anl.gov/mpi. 1995.
[10] Message Passing Interface Forum: MPI-2: Extensions to the Message-Passing Interface. www-unix.mcs.anl.gov/mpi. 1997.
[11] Buyya, R.: High Performance Cluster Computing: Architecture and Systems, Volume 1. Prentice Hall. 1999.
[12] Solsona, F., Giné, F., Hernández, P., Luque, E.: Synchronization methods in distributed processing. IASTED AI’99, pp. 471–473. 1999.
[13] Bailey, D. et al.: The NAS parallel benchmarks. International Journal of Supercomputer Applications. vol. 5 no. 3, pp. 63–73.1991.
[14] Kohl, J.A. and Geist, A.: XPVM 1.0 User’s Guide". Technical Report ORNL/TM-12981, Computer Science and Mathematics Division, Oak Ridge National Laboratory. 1995.
[15] Yan, J.C., Schmidt, M. and Schulbach, C.: The Automated Instrumentation and Monitoring Systems (AIMS) - Version 3.2 User’s Guide". NAS Technical Report NAS-97-001. 1997.
[16] Heath, M.T., Etheridge, J.A.: Visualizing performance of parallel programs. IEEE Software. vol 8 no. 5, pp. 29–39. 1991.
[17] Information Networks Division. HP Co.: Netperf: A Network Performance Benchmark. http://www.netperf.org/netperf/NetperfPage.html. 1996.
[18] Miller, B.P., Hollingsworth, J.K. and Callaghan, M.D.: Environments and Tools for Parallel Scientific Computing. J.J. Dongarra and B. Tourencheau (eds.), SIAM Press. 1994.
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Published
2001-10-01
How to Cite
Solsona Theás, F., Giné de Solà, F., Hernández Budé, P., & Luque Fadón, E. (2001). Coscheduling techniques and monitoring tools for non-dedicated cluster computing. Journal of Computer Science and Technology, 1(05), 15 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/976
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