skip to main content
10.1145/75108.75388acmconferencesArticle/Chapter ViewAbstractPublication PagesmetricsConference Proceedingsconference-collections
Article
Free Access

Parallel simulation of queueing networks: limitations and potentials

Authors Info & Claims
Published:01 April 1989Publication History

ABSTRACT

This paper concerns the parallel simulation of queueing network models (QNMs) using the conservative (Chandy-Misra) paradigm. Most empirical studies of conservative parallel simulation have used QNMs as benchmarks. For the most part, these studies concluded that the conservative paradigm is unsuitable for speeding up the simulation of QNMs, or that it is only suitable for simulating a very limited subclass of these models (e.g., those containing only FCFS servers). In this paper we argue that these are unnecessarily pessimistic conclusions. On the one hand, we show that the structure of some QNMs inherently limits the attainable simulation speedup. On the other hand, we show that QNMs without such limitations can be efficiently simulated using some recently introduced implementation techniques.

We present an analytic method for determining an upper bound on speedup, and use this method to identify QNM structures that will exhibit poor simulation performance. We then survey a number of promising implementation techniques, some of which are quite general in nature and others of which apply specifically to QNMs. We show how to extend the latter to a larger class of service disciplines than had been considered previously.

References

  1. 1.A.O. Allen. Probability, Statistics, and Queueing Theory with Computer Science Applications. Academic Press, New York, NY, 1978. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. 2.R.E. Bryant. Simulation of Packet Communication Architecture Computer Systems. Technical Report MIT, LCS,TR-188, Massachussetts Insitute of Technology, Cambridge, Mass., 1977. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.K.M. Chandy and J. Misra. Asynchronous Distributed Simulation via a Sequence of Parallel Computations. Communications o} the A CM, 24(11):198-206, November 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4.R.M. Fujimoto. Lookahead in Parallel Discrete Event Simulation. In Proc. International Conference on Parallel Processing, St. Charles, IL, August 1988.Google ScholarGoogle Scholar
  5. 5.R.M. Fujimoto. Performance Measurements of Distributed Simulation Strategies. In Distributed Simulation 1988, pages 14-20. Society for Computer Simulation International, San Diego, CA, February 1988.Google ScholarGoogle Scholar
  6. 6.D. Jefferson, B. Beckman, F. Wieland, L. Blume, M. DiLoreto, P. Hontalas, P. Laroche, K. Sturdevant, J. Tupman, V. Warren, J. Wedel, H. Younger, and S. Bellenot. Time Warp Operating System. In Proc. Eleventh A CM Symposium on Operating Systems Principles, pages 77-93, Austin, TX, November 1987. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.D.R. Jefferson. Virtual Time. A CM Transactions on Programming Languages and Systems, 7(3):404-425, July 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.E.D. Lazowska, J. Zahorjan, G.S. Graham, and K.C. Sevcik. Quantitative System Performance: Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cliffs, N:I, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.Y-B. Lin, August 1988. Personal communication.Google ScholarGoogle Scholar
  10. 10.Y-B. Lin, :I-L. Baer, and E.D. Lazowska. Tailoring a Parallel Trace-Driven Simulation Technique to Specific Multiprocessor Cache Coherence Protocols. In Distributed Simulation 1989. Society for Computer Simulation International, San Diego, CA, March 1989.Google ScholarGoogle Scholar
  11. 11.J. Mists. Distributed Discrete-Event Simulation. A CM Computing Surveys, 18(1):39-60, March 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.R.R. Muntz and J.W. Wong. Asymptotic Properties of Closed Queueing Network Models. In Proc. 8th Princeton Conference on Information Sciences and Systems, 1974.Google ScholarGoogle Scholar
  13. 13.D.M. Nicol. Parallel Discrete-Event Simulation of FCFS Stochastic Queueing Networks. In Parallel Programming: Experience with Applications, Languages, and Systems, pages 124-137. ACM SIGPLAN, July 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.:I.K. Peacock, J.W. Wong, and E.G. Manning. Distributed Simulation Using a Network of Processors. Computer Networks, 3(1):44-56, March 1979.Google ScholarGoogle Scholar
  15. 15.D.A. Reed and A.D. Malony. Parallel Discrete Event Simulation: the Chandy-Misra Approach. In Distributed Simulation 1988, pages 8-13. Society for Computer Simulation International, San Diego, CA, February 1988.Google ScholarGoogle Scholar
  16. 16.D.A. Reed, A.D. Malony, and B.D. McCredie. Parallel Discrete Event Simulation Using Shared Memory. IEEE Transactions on Software Engineering, 14(4):541-553, April 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.P.F. Reynolds. A Shared Resource Algorithm for Distributed Simulation. In Proc. 9th International Symposium on Computer Architecture, pages 259-266, Austin, TX, 1982. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18.C.H. Sauer, E.A. MacNair, and S. Salza. A Language for Extended Queueing Networks. IBM Journal of Research and Development, 24(6):747-755, November 1980.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.H.M. Taylor and S. Karlin. An Introduction to Stochastic Modeling. Academic Press, Orlando, FL, 1984.Google ScholarGoogle Scholar
  20. 20.D.B. Wagner. Conservative Parallel Simulation: Principles and Practice. PhD thesis, University of Washington, Seattle, WA, 1989. (In preparation.). Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21.D.B. Wagner and E.D. Lazowsl~. Parallel Simulation of Queueing Networks: Limitations and Potentials. Technical Report 88-09-05, Department of Computer Science, University of Washington, September 1988.Google ScholarGoogle Scholar
  22. 22.D.B. Wagner, E.D. Lazowska, and B.N. Bershad. Techniques for Efficient Shared-Memory Parallel Simulation. In Distributed Simulation 1989. Society for Computer Simulation International, San Diego, CA, March 1989.Google ScholarGoogle Scholar

Index Terms

  1. Parallel simulation of queueing networks: limitations and potentials

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Conferences
                SIGMETRICS '89: Proceedings of the 1989 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
                April 1989
                242 pages
                ISBN:0897913159
                DOI:10.1145/75108

                Copyright © 1989 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 1 April 1989

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • Article

                Acceptance Rates

                Overall Acceptance Rate459of2,691submissions,17%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader