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Workload Characterization Issues and Methodologies

  • Maria Calzarossa
  • Luisa Massari
  • Daniele Tessera
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1769)

Abstract

The performance of any type of system cannot be determined without knowing the workload, that is, the requests being processed. Workload characterization consists of a description of the workload by means of quantitative parameters and functions; the objective is to derive a model able to show, capture, and reproduce the behavior of the workload and its most important features.

Keywords

Execution Time Interarrival Time Parallel Application Task Graph Packet Arrival 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    A.K. Agrawala and J.M. Mohr. A Markovian Model of a Job. In Proc. CPEUG, pages 119–126, 1978.Google Scholar
  2. 2.
    A.K. Agrawala, J.M. Mohr, and R.M. Bryant. An Approach to the Workload Characterization Problem. Computer, pages 18–32, 1976.Google Scholar
  3. 3.
    G.M. Amdahl. Validity of the Single-processor Approach to Achieving Large Scale Computing Capabilities. In Proc. AFIPS Conf., volume 30, pages 483–485, 1967.Google Scholar
  4. 4.
    M.F. Arlitt and C.L. Williamson. Web Server Workload Characterization: The Search for Invariants. In Proc. ACM SIGMETRICS Conf., pages 126–137, 1996.Google Scholar
  5. 5.
    M. Baker, J. Hartman, M. Kupfer, K. Shirriff, and J. Ousterhout. Measurements of a Distributed File System. In Proc. ACM Symposium on Operating Systems Principles, pages 198–212, 1991.Google Scholar
  6. 6.
    J. Beran. Statistical Methods for Data with Long-range Dependence. Statistical Science, 7(4):404–427, 1992.CrossRefGoogle Scholar
  7. 7.
    R.R. Bodnarchuk and R.B. Bunt. A Synthetic Workload Model for a Distributed System File Server. In Proc. ACM SIGMETRICS Conf., pages 50–59, 1991.Google Scholar
  8. 8.
    M. Calzarossa, G. Haring, and G. Serazzi. Workload Modeling for Computer Networks. In U. Kastens and F.J. Ramming, editors, Architekture und Betrieb von Rechensystemen, pages 324–339. Springer-Verlag, 1988.Google Scholar
  9. 9.
    M. Calzarossa, L. Massari, and D. Tessera. Performance Issues of an HPF-like compiler. Future Generation Computer Systems, 1999.Google Scholar
  10. 10.
    M. Calzarossa and G. Serazzi. A Characterization of the Variation in Time of Workload Arrival Patterns. IEEE Trans. on Computers, C-34(2):156–162, 1985.CrossRefGoogle Scholar
  11. 11.
    M. Calzarossa and G. Serazzi. Workload Characterization: a Survey. Proc. of the IEEE, 8(81):1136–1150, 1993.CrossRefGoogle Scholar
  12. 12.
    B.M. Carlson, T.D. Wagner, L.W. Dowdy, and P.H. Worley. Speedup Properties of Phases in the Execution Profile of Distributed Parallel Programs. In R. Pooley and J. Hillston, editors, Modelling Techniques and Tools for Computer Performance Evaluation, pages 83–95. Antony Rowe, 1992.Google Scholar
  13. 13.
    M. Colajanni, P.S. Yu, and D.M. Dias. Analysis of Task Assignment Policies in Scalable Distributed Web-server System. IEEE Trans. on Parallel and Distributed Systems, 9(6), 1998.Google Scholar
  14. 14.
    M.E. Crovella and A. Bestavros. Self-similarity in World Wide Web Traffic: Evidence and Possible Causes. In Proc. ACM SIGMETRICS Conf., pages 160–169, 1996.Google Scholar
  15. 15.
    C.R. Cunha, A. Bestavros, and M.E. Crovella. Characteristics of WWW Clientbased Traces. Technical Report BU-CS-95-010, Computer Science Dept., Boston University, 1995.Google Scholar
  16. 16.
    J. Dilley, R. Friedrich, T. Jin, and J. Rolia. Web Server Performance Measurement and Modeling Techniques. Performance Evaluation, 33(1):5–26, 1998.CrossRefGoogle Scholar
  17. 17.
    D.G. Feitelson and L. Rudolph. Metrics and Benchmarking for Parallel Job Scheduling. In D.G. Feitelson and L. Rudolph, editors, Job Scheduling Strategies for Parallel Processing, volume 1459 of Lecture Notes in Computer Science, pages 1–24. Springer, 1998.CrossRefGoogle Scholar
  18. 18.
    D. Ferrari. Workload Characterization and Selection in Computer Performance Measurement. Computer, 5(4):18–24, 1972.CrossRefGoogle Scholar
  19. 19.
    D. Ferrari. On the Foundations of Artificial Workload Design. In Proc. ACM SIGMETRICS Conf., pages 8–14, 1984.Google Scholar
  20. 20.
    D. Ghosal, G. Serazzi, and S.K. Tripathi. The Processor Working Set and its Use in Scheduling Multiprocessor Systems. IEEE Trans. on Software Engineering, SE-17(5):443–453, 1991.CrossRefGoogle Scholar
  21. 21.
    S.D. Gribble, G.S. Manku, D. Rosselli, E.A. Brewer, T.J. Gibson, and E.L. Miller. Self-similarity in File Systems. In Proc. ACM SIGMETRICS Conf., pages 141–150, 1998.Google Scholar
  22. 22.
    R. Gusella. A Measurement Study of Diskless Workstation Traffic on an Ethernet. IEEE Trans. on Communications, COM-38(9):1557–1568, 1990.CrossRefGoogle Scholar
  23. 23.
    G. Haring. On Stochastic Models of Interactive Workloads. In A.K. Agrawala and S.K. Tripathi, editors, PERFORMANCE’ 83, pages 133–152. North-Holland, 1983.Google Scholar
  24. 24.
    M.T. Heath, A.D. Malony, and D.T. Rover. Parallel Performance Visualization: from Practice to Theory. IEEE Parallel and Distributed Technology, 3(4):44–60, 1995.CrossRefGoogle Scholar
  25. 25.
    R. Hofmann, R. Klar, B. Mohr, A. Quick, and M. Siegle. Distributed Performance Monitoring: Methods, Tools, and Applications. IEEE Trans. on Parallel and Distributed Systems, 5(6):585–598, 1994.CrossRefGoogle Scholar
  26. 26.
    R. Jain and S.A. Routhier. Packet Trains-Measurements and a New Model for Computer Network Traffic. IEEE Journal on Selected Areas in Communications, SAC-4(6):986–995, 1986.CrossRefGoogle Scholar
  27. 27.
    J.L. Jerkins and J.L. Wang. Cell-level Measurement Analysis of Individual ATM Connections. Workshop on Workload Characterization in High-Performance Computing Environments, 1998. http://sokrates.ani.univie.ac.at/~gabi/wlc.mascots.
  28. 28.
    T. Le Blanc, J. Mellor-Crummey, and R. Fowler. Analyzing Parallel Program Executions Using Multiple Views. Journal of Parallel and Distributed Computing, 9(2):203–217, 1990.CrossRefGoogle Scholar
  29. 29.
    W.E. Leland, M.S. Taqqu, W. Willinger, and D.V. Wilson. On the Self-similar Nature of Ethernet Traffic (Extended Version). IEEE/ACM Trans. on Networking, 2(1):1–15, 1994.CrossRefGoogle Scholar
  30. 30.
    P.A. Lewis and G.S. Shedler. Statistical Analysis of Non-stationary Series of Events in a Data Base System. IBM Journal on Research and Development, 20:465–482, 1976.zbMATHMathSciNetCrossRefGoogle Scholar
  31. 31.
    B.A. Mah. An Empirical Model of HTTP Network Traffic. In Proc. IEEE InfoCom’ 97, 1997.Google Scholar
  32. 32.
    S. Majumdar, D. Eager, and R. Bunt. Characterization of Programs for Scheduling in Multiprogrammed Parallel Systems. Performance Evaluation, 13(2):109–130, 1991.zbMATHCrossRefMathSciNetGoogle Scholar
  33. 33.
    B.B. Mandelbrot and J.W. Van Ness. Fractional Brownian Motions, Fractional Noises and Applications. SIAM Review, 10:422–437, 1968.zbMATHCrossRefMathSciNetGoogle Scholar
  34. 34.
    A. Merlo and P.H. Worley. Analyzing PICL trace data with MEDEA. In G. Haring and G. Kotsis, editors, Computer Performance Evaluation, volume 794 of Lecture Notes in Computer Science, pages 445–464. Springer-Verlag, 1994.Google Scholar
  35. 35.
    N. Nieuwejaar, D. Kotz, A. Purakayastha, C. Ellis, and M. Best. File-access Characteristics of Parallel Scientific Workloads. IEEE Trans. on Parallel and Distributed Systems, 7(10):1075–1088, 1996.CrossRefGoogle Scholar
  36. 36.
    V. Paxson. Growth Trends in Wide-area TCP Connections. IEEE Network, 8(4):8–17, 1994.CrossRefGoogle Scholar
  37. 37.
    V. Paxson and S. Floyd. Wide-area Traffic: The Failure of Poisson Modeling. IEEE/ACM Trans. on Networking, 3(3):226–244, 1995.CrossRefGoogle Scholar
  38. 38.
    S.V. Raghavan, P.J. Joseph, and G. Haring. Workload Models for Multiwindow Distributed Environments. In H. Beilner and F. Bause, editors, Quantitative Evaluation of Computing and Communication Systems, pages 314–326. Springer, 1995.Google Scholar
  39. 39.
    S.V. Raghavan, D. Vasukiammaiyar, and G. Haring. Generative Networkload Models for a Single Server Environment. In Proc. ACM SIGMETRICS Conf., pages 118–127, 1994.Google Scholar
  40. 40.
    E. Rosti, G. Serazzi, E. Smirni, and M. Squillante. The Impact of I/O on Program Behavior and Parallel Scheduling. In Proc. ACM SIGMETRICS Conf., pages 56–65, 1998.Google Scholar
  41. 41.
    G. Serazzi. A Functional and Resource-oriented Procedure for Workload Modeling. In F.J. Kylstra, editor, PERFORMANCE’ 81, pages 345–361. North-Holland, 1981.Google Scholar
  42. 42.
    K. Sevcik. Characterization of Parallelism in Applications and Their Use in Scheduling. In Proc. ACM SIGMETRICS Conf., pages 171–180, 1989.Google Scholar
  43. 43.
    K.C. Sevcik. Application Scheduling and Processor Allocation in Multiprogrammed Parallel Processing Systems. Performance Evaluation, 19:107–140, 1994.zbMATHCrossRefGoogle Scholar
  44. 44.
    J.F. Shoch and J.A. Hupp. Measured Performance of an Ethernet Local Network. Communications of the ACM, 23(12):711–721, 1980.CrossRefGoogle Scholar
  45. 45.
    E. Smirni and D. Reed. Lessons from characterizing the input/output behavior of parallel scientific applications. Performance Evaluation, 33(1):27–44, 1998.CrossRefGoogle Scholar
  46. 46.
    K. Sreenivasan and A.J. Kleinman. On the Construction of a Representative Synthetic Workload. Communications of the ACM, 17(3):127–133, 1974.CrossRefGoogle Scholar
  47. 47.
    D. Tessera, M. Calzarossa, and A. Malagoli. Performance Analysis of a Parallel Hydrodynamic Application. In A. Tentner, editor, High Performance Computing, pages 33–38. SCS Press, 1998.Google Scholar
  48. 48.
    A. Waheed and J. Yan. Workload Characterization of CFD Applications Using Partial Differential Equation Solvers. Workshop on Workload Characterization in High-Performance Computing Environments, 1998. http://sokrates.ani.univie.ac.at/~gabi/wlc.mascots.
  49. 49.
    W. Willinger, M.S. Taqqu, R. Sherman, and D.V. Wilson. Self-similarity Through High-variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level. In Proc. ACM SIGCOMM Conf., pages 100–113, 1995.Google Scholar
  50. 50.
    M. Zhou and A.J. Smith. Tracing Windows95. Technical Report, Computer Science Division, UC Berkeley, November 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Maria Calzarossa
    • 1
  • Luisa Massari
    • 1
  • Daniele Tessera
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di PaviaPaviaItaly

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