Skip to main content
Log in

A Performance Comparison of Coscheduling Strategies for Workstation Clusters

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Workstation clusters are emerging as a general-purpose computing platform for the execution of workloads comprising parallel and sequential applications. The scalability and flexibility typical of implicit coscheduling strategies makes them a very promising solution to the scheduling needs of workstation clusters. In this paper we present a simulation study that compares, for a variety of workloads (that include both parallel and sequential applications) and operating system schedulers, 12 implicit coscheduling strategies in terms of the performance they are able to deliver to applications. By using a detailed simulator, we evaluate the performance of different coscheduling alternatives for a variety of simulation scenarios, and we identify the set of strategies that deliver the best performance to all the applications composing typical cluster workloads. Moreover, we show that for schedulers providing immediate preemption, the best strategies are also the simplest ones to implement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R.H. Arpaci, A.C. Dusseau, A.M. Vahdat, L. Liu, T.E. Anderson and D.A. Patterson, The interaction of parallel and sequential workloads on a network of workstations, in: Proc. of ACM SIGMETRICS'95 (May 1995) pp. 267–278.

  2. M.J. Atallah, C.L. Black, D.C. Marinescu, H.J. Siegel and T.L. Casavant, Models and algorithms for co-scheduling compute-intensive tasks on a network of workstations, Journal of Parallel and Distributed Computing 16 (1992) 319–327.

    Google Scholar 

  3. D. Bailey, T. Harris, R. Der Wigngaart, W. Saphir, W. Woo and M. Yarrow, The NAS parallel benchmarks 2.0, Technical Report NAS-95–010, NASA Ames Research Center (1995).

  4. R. Buyya, High Performance Cluster Computing (Prentice Hall, 1999).

  5. G. Chiola and G. Ciaccio, A performance-oriented operating system approach to fast communications in a cluster of personal computers, in: Proc. Int. Conf. on Parallel and Distributed Processing Techniques and Applications, Las Vegas, NV (July 1998).

  6. DAVID: Distributed Architecture VIdeo on Demand system, Faculty of Engineering, University of Catania, http://sun195.iit. unict.it/Vod.

  7. M.V. Devarakonda and R.K. Iyer, Predictability of process resource usage: A measurement-based study on Unix, IEEE Transactions on Software Engineering 15(12) (December 1989).

  8. A.B. Downey and M. Harchol-Balter, Exploiting process lifetime distributions for dynamic load balancing, ACM Transactions on Computer Systems 15(3) (August 1997) 253–385.

    Google Scholar 

  9. X. Du and X. Zhang, Coordinating parallel processes on Networks of workstations, Journal of Parallel and Distributed Computing 46(2) (1997).

  10. A.C. Dusseau, R.H. Arpaci and D.E. Culler, Effective distributed scheduling of parallel workloads, in: Proc. of ACM SIGMETRICS'96 (1996).

  11. A.C. Dusseau-Arpaci, D.E. Culler and A.M. Mainwaring, Scheduling with implicit information in distributed systems, in: Proc. of ACM SIGMETRICS'98 (1998).

  12. D.G. Feitelson and L. Rudolph, Gang scheduling performance bene-fits for fine-grained synchronization, Journal of Parallel and Distributed Computing 16(4) (December 1992) 306–318.

    Google Scholar 

  13. GAMMA homepage, http://www.disi.unige.it/ project/gamma.

  14. M. Harchol-Balter, M.E. Crovella and C.D. Murta, On choosing a task assignment policy for a distributed server system, Journal of Parallel and Distributed Computing 59 (1999) 204–228.

    Google Scholar 

  15. A.R. Karlin, M.S. Manasse, L.A. McGeoch and S. Owicki, Competitive randomized algorithms for nonuniform problems, Algorithmica 11(6) (June 1994) 542–571.

    Google Scholar 

  16. D. Kotz, S.B. Toh and S. Radhakrishnan, A detailed simulation model of the HP 97569 disk drive, Technical Report PCS-TR94–220, Department of Computer Science, Dartmouth College (1994).

  17. W.E. Leland and T.J. Ott, Load-balancing heuristics and process behavior, in: Proc. of ACM SIGMETRICS’ 86 (1986) pp. 54–69.

  18. R. Mukherjee, A scalable and highly available clustered Web server, in: High Performance Cluster Computing, ed. R. Buyya (Prentice Hall, 1999) Ch. 36, pp. 811–840.

  19. S. Nagar, A. Banerjee, A. Sivasubramaniam and C.R. Das, Alternatives to coscheduling a network of workstations, Journal of Parallel and Distributed Computing 59(2) (November 1999) 302–327.

    Google Scholar 

  20. J.K. Ousterhout, Scheduling techniques for concurrent systems, in: Proc. of 3rd Int. Conf. on Distributed Computing Systems (May 1982) pp. 22–30.

  21. F. Petrini and W. Feng, Buffered coscheduling: A new methodology for multitasking parallel jobs on distributed systems, in: Proc. of IPDPS 2000, Cancun, Mexico (May 2000).

  22. P.G. Sobalvarro, S. Pakin, W.E. Weihl and A.A. Chien, Dynamic coscheduling on workstation clusters, in: Proc. of IPPS’ 98 Workshop on Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, Vol. 1459 (Springer, 1998).

  23. SUN Microsystems, Solaris 2.6 Software Developer Collection, Vol. 1, System Interface Guide (1997).

  24. U. Vahalia, Unix Internals: The New Frontiers (Prentice Hall, 1996).

  25. T. von Eicken, A. Basu, V. Buch and W. Vogels, U-Net: A user-level network interface for parallel and distributed computing, in: Proc. of the 15th ACM Symp. on Operating Systems Principles (SOSP’ 95), Copper Mountain, CO (December 1995).

  26. Y. Zhang, A. Sivasubramaniam, J. Moreira and H. Franke, A simulation-based study of scheduling mechanisms for a dynamic cluster environment, in: Proc. of the 14th ACM Int. Conference on Supercomputing (May 2000).

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Anglano, C. A Performance Comparison of Coscheduling Strategies for Workstation Clusters. Cluster Computing 4, 121–131 (2001). https://doi.org/10.1023/A:1011416914729

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011416914729

Navigation