Skip to main content
SpringerLink
Log in
Book cover

Handbook of Nature-Inspired and Innovative Computing pp 575–611Cite as

Predicting Grid Resource Performance Online

  • Chapter

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abilene. http://www.ucaid.edu/abilene/.

    Google Scholar 

  2. B. Allcock, J. Bester, J. Bresnahan, A. L. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnal, and S. Tuecke (2002): Data management and transfer in high performance computational grid environments. Parallel Computing Journal, 28(5), 749–771.

    Google Scholar 

  3. B. Allock, I. Foster, V. Nefedova, A. Chervenak, E. Deelman, C. Kesselman, J. Leigh, A. Sim, and A. Shoshani (2001): High-performance remote access to climate simulation data: A challenge problem for data grid technologies. In Proceedings of IEEE SC’01 Conference on High-performance Computing. http://www.globus.org/research/papers*/sc01ewa_esg_chervenak_final.pdf.

    Google Scholar 

  4. M. Allen and R. Wolski. Adaptive timeout discovery using the network weather service. In Proceedings of HPDC-11, July 2002. http://www.cs.ucsb.edu/~rich/publications/nws-adapt.pdf.

    Google Scholar 

  5. M. Allen and R. Wolski (2003): The livny and plank-beck problems: Studies in data movement on the computational grid. In Proceedings of SC03.

    Google Scholar 

  6. H. Balakrishnan, M. Stemm, S. Seshan, and R. H. Katz (1997): Analyzing stability in wide-area network performance. In Measurement and Modeling of Computer Systems, pp. 2–12.

    Google Scholar 

  7. F. Berman, A. Chien, K. Cooper, J. Dongarra, I. Foster, L. J. Dennis Gannon, K. Kennedy, C. Kesselman, D. Reed, L. Torczon, and R. Wolski (2001): The GrADS project: Software support for high-level grid application development. International Journal of High-performance Computing Applications, 15(4), 327–344.

    Google Scholar 

  8. F. Berman, G. Fox, and T. Hey (2003): Grid Computing: Making the Global Infrastructure a Reality. Wiley and Sons.

    Google Scholar 

  9. F. Berman, R. Wolski, S. Figueira, J. Schopf, and G. Shao (1996): Application level scheduling on distributed heterogeneous networks. In Proceedings of Supercomputing.

    Google Scholar 

  10. The BOINC project. http://boinc.berkeley.edu.

    Google Scholar 

  11. J. Brevik, D. Nurmi, and R. Wolski (2004): Quantifying machine availability in networked and desktop grid systems. In Proceedings of CCGrid04.

    Google Scholar 

  12. H. Casanova, G. Obertelli, F. Berman, and R. Wolski (2000): The AppLeS Parameter Sweep Template: User-Level Middleware for the +Grid. In Proceedings of IEEE SC’00 Conference on High-performance Computing.

    Google Scholar 

  13. W. Chrabakh and R. Wolski. GrADSAT: A Parallel SAT Solver for the Grid. In Proceedings of IEEE SC03, November 2003.

    Google Scholar 

  14. H. Cramer (1946): Mathematical Methods of Statistics. Princeton University Press.

    Google Scholar 

  15. K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman (2001): Grid information services for distributed resource sharing. In Proceedings 10th IEEE Symp. on High Performance Distributed Computing.

    Google Scholar 

  16. C. Dovrolis, D. Moore, and P. Ramanathan (2001): What do packet dispersion techniques measure? In Proceedings of Infocom.

    Google Scholar 

  17. A. Downey (1999): Using pchar to estimate internet link characteristics. In Proceedings of ACM SIGCOMM.

    Google Scholar 

  18. The Entropia Home Page. http://www.entropia.com.

    Google Scholar 

  19. I. Foster and C. Kesselman (1997): Globus: A metacomputing infrastructure toolkit. International Journal of Supercomputer Applications.

    Google Scholar 

  20. I. Foster and C. Kesselman (1998): The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers.

    Google Scholar 

  21. I. Foster, C. Kesselman, J. Nick, and S. Tuecke. The physiology of the grid: An open grid services architecture for distributed systems integration. http://www.globus.org/research/papers/ogsa.pdf.

    Google Scholar 

  22. Globus. http://www.globus.org.

    Google Scholar 

  23. GrADS. http://hipersoft.cs.rice.edu/grads.

    Google Scholar 

  24. C. Granger and P. Newbold (1986): Forecasting Economic Time Series. Academic Press.

    Google Scholar 

  25. T. Heath, R. Martin, and T. Nguyen (2001): The shape of failure. In Proceedings of the First Workshop on Evaluating and Architecting System Dependability.

    Google Scholar 

  26. The iperf tool: http://dast.nlanr.net/Projects/Iperf.

    Google Scholar 

  27. V. Jacobson (1988): Congestion avoidance and control. In Proceedings of SIGCOMM’ 88, 18.

    Google Scholar 

  28. R. Jones. The netperf tool: http://www.netperf.org/netperf/NetperfPage.html.

    Google Scholar 

  29. C. Krintz and R. Wolski (2001): Nwsalarm: A tool for accurately detecting degradation in expected performance of grid resources. In Proceedings of CCGrid01.

    Google Scholar 

  30. W. E. Leland, M. S. Taqq, W. Willinger, and D. V. Wilson (1993): On the self-similar nature of Ethernet traffic. In D. P. Sidhu, editor, ACM SIGCOMM, pp. 183–193, San Francisco, California.

    Google Scholar 

  31. D. Long, A. Muir, and R. Golding (1995): A longitudinal survey of internet host reliability. In 14th Symposium on Reliable Distributed Systems, pp. 2–9.

    Google Scholar 

  32. The nsf middleware initiative — http://www.nsf-middleware.org.

    Google Scholar 

  33. New ttcp: http://www.leo.org/~elmar/nttcp.

    Google Scholar 

  34. D. Nurmi, J. Brevik, and R. Wolski (2005): Modeling machine availability in enterprise and wide-area distributed computing environments. Proceedings of European Conference on Parallel Computing (EUROPAR) August, 2005.

    Google Scholar 

  35. The network weather service home page — http://nws.cs.ucsb.edu.

    Google Scholar 

  36. V. Paxon and S. Floyd (1997): Why we don’t know how to simulate the internet. In Proceedings of the Winder Communication Conference, also citeseer.nj.nec.com/paxon97why.html.

    Google Scholar 

  37. V. Paxson and S. Floyd. Wide area traffic: the failure of Poisson modeling. IEEE/ACM Transactions on Networking, 3(3), 226–244.

    Google Scholar 

  38. A. Petitet, S. Blackford, J. Dongarra, B. Ellis, G. Fagg, K. Roche, and S. Vadhiyar (2001): Numerical libraries and the grid. In Proceedings of IEEE SC’01 Conference on High-performance Computing.

    Google Scholar 

  39. The planetLab home page. http://www.planet-lab.org.

    Google Scholar 

  40. J. S. Plank, S. Atchley, Y. Ding, and M. Beck (2002): Algorithms for high performance, wide-area, distributed file downloads. Technical Report UTCS-02-485, Department of Computer Science, University of Tennessee. http://www.cs.utk.edu/~plank/plank/papers/CS-02-485.html.

    Google Scholar 

  41. P. Primet, R. Harakaly, and F. Bonnassieux (2002): Experiments of network throughput measurement and forecasting using the network weather service. In Workshop on Global and Peer-to-Peer Computing on Large Scale Distributed Systems.

    Google Scholar 

  42. M. Ripeanu, A. Iamnitchi, and I. Foster (2001): Cactus application: Performance predictions in a grid environment. In Proceedings of European Conference on Parallel Computing (EuroPar) 2001.

    Google Scholar 

  43. N. Spring and R. Wolski (1998): Application level scheduling: Gene sequence library comparison. In Proceedings of ACM International Conference on Supercomputing 1998.

    Google Scholar 

  44. M. Swany and R. Wolski (2002): Building performance topologies for computational grids. In Proceedings of Los Alamos Computer Science Institute (LACSI) Symposium, 2002.

    Google Scholar 

  45. M. Swany and R. Wolski (2002): Multivariate resource performance forecasting in the network weather service. In Proceedings of IEEE SC’02 Conference on High-performance Computing.

    Google Scholar 

  46. T. Tannenbaum and M. Litzkow (1995): The condor distributed processing system. Dr. Dobbs Journal.

    Google Scholar 

  47. The TeraGrid Home Page. http://www.teragrid.org.

    Google Scholar 

  48. S. Vazhkudai, J. Schopf, and I. Foster (2002): Predicting the performance of wide-area data transfers. In Proceedings of IEEE International Parallel and Distributed Parallel Systems Conference.

    Google Scholar 

  49. R. Wolski (1998): Dynamically forecasting network performance using the network weather service. Cluster Computing, 1, 119–132.

    Article  Google Scholar 

  50. R. Wolski (2003): Experiences with predicting resource performance online in computational grid settings. ACM SIGMETRICS Performance Evaluation Review, 30(4), 41–49.

    Google Scholar 

  51. R. Wolski, J. Brevik, C. Krintz, G. Obertelli, N. Spring, and A. Su (2001): Writing programs that run everyware on the computational grid. IEEE Transactions on Parallel and Distributed Systems, 12(10), 1066–1080.

    Article  Google Scholar 

  52. R. Wolski, N. Spring, and J. Hayes (1999): The network weather service. A distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems, 15(5–6), 757–768.

    Google Scholar 

  53. Y. Zhang, N. Du, V. Paxson, and S. Shenker (2001): The constancy of internet path properties. In Proceedings of ACM SIGCOMM Internet Measurement Workshop.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of California, Santa Barbara

    Rich Wolski, Graziano Obertelli, Matthew Allen, Daniel Nurmi & John Brevik

Authors
  1. Rich Wolski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graziano Obertelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Nurmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John Brevik
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The University of Sydney, Australia

    Albert Y. Zomaya

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Wolski, R., Obertelli, G., Allen, M., Nurmi, D., Brevik, J. (2006). Predicting Grid Resource Performance Online. In: Zomaya, A.Y. (eds) Handbook of Nature-Inspired and Innovative Computing. Springer, Boston, MA. https://doi.org/10.1007/0-387-27705-6_18

Download citation

  • DOI: https://doi.org/10.1007/0-387-27705-6_18

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-40532-2

  • Online ISBN: 978-0-387-27705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation