An Optimized Architecture for Supporting Data Streaming in Interactive Grids

Chapter

Abstract

The access to instrumentation and support of real-time requirements were already envisaged, in terms of time-sensitive control of devices and remote communications, in the original grid vision. Nevertheless, the integration of devices concretely rises the problem of supporting such operations with proper network requirements. On the other hand, as an evolution of peer-to-peer (p2p) file-sharing applications, overlay-based networks can be utilized to exploit application level strategies to overcome limitations imposed by the underlying network infrastructure, e.g., the lack of multicast support.

In this perspective, the chapter introduces an overlay Content Distribution Network (CDN) able to sustain the real-time delivery of data streams to support interactive Grid applications. To better use resources the control and optimization of the CDN are performed through a model predictive control scheme. Simulations are provided to show the effectiveness of the proposed solution.

Keywords

Peer-to-peer Overlays Optimization Streaming Content Distribution Networks 

References

  1. 1.
    I. Foster, C. Kesselman, and S. Tuecke Anatomy of the Grid. International Journal of Supercomputer Applications, 15(3), 2001Google Scholar
  2. 2.
    S. Tuecke, K. Czajkowski, I. Foster, S. Graham, C. Kesselman, P. Vanderbilt, and D. Snelling. Grid Service Specification. Open Grid Service Infrastructure Working Group (OGSI), Global Grid Forum, 2003.Google Scholar
  3. 3.
    I. Foster, and C. Kesselman. The grid: Blueprint for a new computing Infrastructure, Morgan Kaufmann, San Francisco 1999.Google Scholar
  4. 4.
    S. Vignola, and S. Zappatore. The LABNET-server software architecture for remote management of distributed laboratories: current status and perspectives. In Distributed cooperative laboratories, issues in networking, instrumentation and measurement, F. Davoli, S. Palazzo, S. Zappatore Eds., Springer, USA, pp. 435–450, 2006.CrossRefGoogle Scholar
  5. 5.
    L. Caviglione, F. Davoli, P. Molini, and S. Zappatore. Design and preliminary analysis of a framework for integrating real and virtual instrumentation within a grid infrastructure. Proceedings International Symposium on Performance Evaluation of Computer and Telecommunication System (SPECTS’06), Calgary, AB, July 2006Google Scholar
  6. 6.
    L. Caviglione, and L. Veltri. A p2p framework for distributed and cooperative laboratories, 2005 Tyrrhenian international workshop on digital communications, Sorrento, Italy, July 2005. In Distributed cooperative laboratories - networking, instrumentation and measurements, F. Davoli, S. Palazzo, S. Zappatore, Eds., Springer, Norwell, MA, pp. 309–319, 2006CrossRefGoogle Scholar
  7. 7.
    S. Sen and J. Wang. Analysing Peer-To-Peer traffic across large networks. IEEE/ACM Transactions on Networking, 12(2), 219–232, April 2004CrossRefGoogle Scholar
  8. 8.
    M. Zhang, Q. Zhang, L. Sun, and S. Yang. Understanding the power of pull-based streaming protocols: Can we do better?. IEEE Journal on Selected Areas in Communications, 25(9) 1678–1694, December 2007CrossRefGoogle Scholar
  9. 9.
    X. Zhang, J. Liu, B. Li, and T.-S.P. Yum. Coolstreaming/donet: A data-driven overlay network for efficient media streaming. IEEE INFOCOM 2005, Miami, FL, March 2005.Google Scholar
  10. 10.
    J. Li, P.A. Chou, and C. Zhang. Mutualcast: An efficient mechanism for one-to-many content distribution. Proceedings of ACM SIGCOMM - Asia Workshop 2004, Beijing, 2004Google Scholar
  11. 11.
    S. Chul Han, and Y. Xia. Network load-aware content distribution in overlay networks. Computer Communications, 32(1), 51–61 January 2009CrossRefGoogle Scholar
  12. 12.
    C. Wu, and B. Li. Optimal rate allocation in overlay content distribution. Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet (NETWORKING 2007), in Lecture Notes in Computer Science, Springer, USA, Vol. 4479/2007 pp. 678–690, 2007Google Scholar
  13. 13.
    N. Laoutaris, V. Zissimopoulos, and I. Stavrakakis. On the optimization of storage capacity allocation for content distribution. Computer Networks, 47(3) 409–428, February 2005CrossRefGoogle Scholar
  14. 14.
    N. Laoutaris, V. Zissimopoulos, and I. Stavrakakis. Joint object placement and node dimensioning for internet content distribution. Information Processing Letters, 89(6) 273–279, March 2004MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    D. Bertsekas. Dynamic programming and optimal control (2nd Edition), Athena Scientific, Belmont, 2000Google Scholar
  16. 16.
    C. Cervellera and M. Muselli. Efficient sampling in approximate dynamic programming algorithms. Computational Optimization and Applications, 38(3) 417–443, December 2007MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    F. Allgöwer and A. Zheng. Nonlinear model predictive control. In Applications of nonlinear predicticve control, F. Allgöwer, A. Zheng, Eds., Basel, Birkhäuser, Switzerland, 2000Google Scholar
  18. 18.
    A. Törn, and A. Žilinskas. Global optimization, Springer, Berlin, 1989MATHCrossRefGoogle Scholar
  19. 19.
    H. Niederreiter. Random number generation and Quasi-Monte Carlo methods, SIAM, Philadelphia, PA, 1992MATHCrossRefGoogle Scholar
  20. 20.
    J. van der Meer, D. Mackie, V. Swaminathan, D. Singer, and P. Gentric. RTP payload format for transport of MPEG-4 elementary streams. Network Working Group, IETF, RFC 3640, November 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Institute of Intelligent Systems for Automation (ISSIA)GenovaItaly
  2. 2.Institute of Intelligent Systems for Automation (ISSIA)GenovaItaly

Personalised recommendations