350 rub
Journal Neurocomputers №11 for 2009 г.
Article in number:
Methods of data transfer speed estimation in the data grid based on linear regression
Keywords: available band-width for the network channel
Authors:
А. T. Vachitov, O. N. Granichin, M. A. Pan-shenskov
Abstract:
The paper is devoted to the problem of available bandwidth estimation for the network channels in distributed computing systems, in particular in learning distributed artificial neural nets. The linear model of estimation is described, four methods are proposed to solve the problem. The methods are compared by simulation, and the conclusion about the most appropriate method is derived.
Pages: 45-52
References
  1. Bo, Y., Xun,W.,Research on the performance of grid computing for distributed neural networks // International Journal of Computer Science and Netwrok Security. 2006. V. 6. No. 4. P. 179 - 187.
  2. Milea, C. and Svasta, P., Using distributed neural networks in automated optical inspection // Concurrent Engineering in Electronic Packaging, 24rd Int. spring seminar on electronics technology // Calimanesti-Caciulata, Romania. 2001. May.
    P. 286-288.
  3. Venugopal, S., Buyya, R.,and Ramamohanarao, K., A taxonomy of data grids for distributed data sharing, management, and processing // ACM Computing Surveys. 2006. V. 38. Issue 1. P. 1-53.
  4. Ranganathan, K.,andFoster, I., Decoupling computation and data scheduling in distributed data-intensive applications // In proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, 2002. July 24-26. P. 352.
  5. Neginhal, M., Efficient Estimation of Available Bandwidth Along Network Paths // Master-s Thesis, North Carolina State University. 2006.
  6. Keshav, S., A Control-Theoretic Approach to Flow Control // ACM SIGCOMM Computer Communication Review. 1995. V. 5. Issue 1. P. 188-201.
  7. Paxson, V., End-to-end internet packet dynamics. // IEEE/ACM Transactions on Networking (TON), 1999. V. 7. No. 3.
    P. 277-292.
  8. Ribeiro, V., Riedi, R., Baraniuk, R., Navratil, J.,and Cottrell, L., pathChirp: Efficient Available Bandwidth Estimation for Network Paths // In Proceedings of The Conference on Passive and Active Measurements (PAM). 2003. April.
  9. Carter, R. L. and Crovella, M. E., Measuring bottleneck link speed in packet-switched networks // Performance Evaluation, 1996. 27(28). P. 297-318.
  10. Lai, K. and Baker, M., Nettimer: A tool for measuring bottleneck link bandwidth // In Proceedings of the USENIX Symposium on Internet Technologies and Systems. 2001. P. 123-134.
  11. Dovrolis, C., Ramanathan, P.,and Moore, D., Packet-dispersion techniques and a capacity estimation methodology // IEEE/ACM Transactions on Networking (TON). 2004. V. 12. No. 6. P. 963-977.
  12. Kapoor, R., Chen, L.-J., Lao, L., Gerla, M.,and Sanadidi, M. Y., CapProbe: a simple and accurate capacity estimation technique // ACM SIGCOMM Computer Communication Review. 2004. V. 34. No. 4. P. 67-78.
  13. Carter, R. L., Crovella, M. E., Dynamic server selection using bandwidth probing in wide-area networks // Technical Report. 1996.
  14. Hu, N. and Steenkiste, P., Evaluation and characterization of available bandwidth and probing techniques // IEEE JSAC Special Issue in Internet and WWW Measurement, Mapping, and Modeling. 21. 2003. P. 879-894.
  15. Jain, M. and Dovrolis, C., End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput // IEEE/ACM Transactions on Networking. 2003. V. 11. No. 4. P. 537-549.
  16. Strauss, J., Katabi, D.,and Kaashoek, F., A Measurement Study of Available Bandwidth Estimation Tools. // In ACM/USENIX internet measurement conference (IMC). 2003. P. 39-44.
  17. Seshan,S., Stemm, M., and Katz, R. H., SPAND: Shared passive network performance discovery // In USENIX symposium on internet technologies and systems. 1997.
  18. Vazhkudai, S. and Schopf, J. M., Predicting Sporadic Grid Data Transfers // In Proceedings of HPDC-2002. 2002.
  19. Guo, L., Stability of recursive stochastic tracking algorithms. // SIAM J. control and optimization. 1994. V. 32. No. 5.
    P. 1195-1225.
  20. Granichi, O. N., Linear regression and filtering under nonstandard assumptions (Arbitrary noise) // IEEE Trans. Automat. Contr. 2004. V. 49. No. 10. P. 1830-1835.