Advertisement

Computing on the Edge: A Platform for Replicating Internet Applications

  • Michael Rabinovich
  • Zhen Xiao
  • Amit Aggarwal
Conference paper

Abstract

Content delivery networks (CDNs) improve the scalability of accessing static and, recently, streaming content. However, proxy caching can improve access to these types of content as well. A unique value of CDNs is therefore in improving performance of accesses to dynamic content and other computer applications. We describe an architecture, algorithms, and a preliminary performance study of a CDN for applications (ACDN). Our system includes novel algorithms for automatic redeployment of applications on networked servers as required by changing demand and for distributing client requests among application replicas based on their load and proximity. The system also incorporates a mechanism for keeping application replicas consistent in the presence of developer updates to the content. A prototype of the system has been implemented.

Keywords

Content Delivery Network Target Server Proxy Cache Herd Effect Replica Placement 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A. A. Awadallah and M. Rosenblum. The vMatrix: A network of virtual machine monitors for dynamic content distribution. In 7th Int. Workshop on Web Content Caching and Distribution (WCW 2002), Aug. 2002.Google Scholar
  2. [2]
    A. Biliris, C. Cranor, F. Douglis, M. Rabinovich, S. Sibal, O. Spatscheck, and W. Sturm. CDN brokering. In 6th Int. Workshop on Web Caching and Content Distribution, June 2001.Google Scholar
  3. [3]
    I. Cidon, S. Kutten, and R. Soffer. Optimal allocation of electronic content. In Proceedings of IEEE INFOCOM, pages 1773–1780, Los Alamitos, CA, Apr. 22–26 2001. IEEE Computer Society.Google Scholar
  4. [4]
    M. Dahlin. Interpreting stale load information. IEEE Transactions on Parallel and Distributed Systems, 11(10):1033–1047, Oct. 2000.CrossRefGoogle Scholar
  5. [5]
    F. Douglis, A. Haro, and M. Rabinovich. HPP: HTML macro-preprocessing to support dynamic document caching. In Proceedings of the Symposium on Internet Technologies and Systems, pages 83–94. USENIX, Dec. 1997.Google Scholar
  6. [6]
    Ejasent, Inc. Ejasent web site. http://www.ejasent.com/, 2003.
  7. [7]
    Z. Fei, S. Bhattacharjee, E. W. Zegura, and M. H. Ammar. A novel server selection technique for improving the response time of a replicated service. In INFOCOM, pages 783–791, 1998.Google Scholar
  8. [8]
    S. Gadde, J. Chase, and M. Rabinovich. Web caching and content distribution: A view from the interior. In 5th Int. Web Caching and Content Delivery Workshop (WCW5), 2000.Google Scholar
  9. [9]
    A. V. Hoff, J. Payne, and S. Shaio. Method for the distribution of code and data updates. U.S. Patent Number 5,919,247, July 6 1999.Google Scholar
  10. [10]
    A. Iyengar and J. Challenger. Improving Web server performance by caching dynamic data. In Proceedings of the USENIX Symposium on Internet Technologies and Systems, pages 49–60, Berkeley, Dec. 8–11 1997.Google Scholar
  11. [11]
    J. Kangasharju, J. W. Roberts, and K. W. Ross. Object replication strategires in content distribution networks. In Proceedings of the Sixth Int Workshop on Web Caching and Content Distribution (WCW), 2001.Google Scholar
  12. [12]
    P. Karbhari, M. Rabinovich, Z. Xiao, and F. Douglis. ACDN: a content delivery network for applications (project demo). In Proceedings of ACM SIGMOD, pages 619–619, June 2002.Google Scholar
  13. [13]
    A. Leff, J. L. Wolf, and P. S. Yu. Replication algorithms in a remote caching architecture. IEEE Transactions on Parallel and Distributed Systems, 4(11):1185–1204, Nov. 1993.CrossRefGoogle Scholar
  14. [14]
    Oracle Corporation and Akamai Technologies, Inc. ESI — accelerating e-business applications. http://www.esi.org/, 2001.
  15. [15]
    G. Pierre, I. Kuz, M. van Steen, and A. S. Tanenbaum. Differentiated strategies for replicating Web documents. Computer Communications, 24(2):232–240, Feb. 2001.CrossRefGoogle Scholar
  16. [16]
    G. Pierre and M. van Steen. Globule: a platform for self-replicating Web documents. In 6th Int. Conference on Protocols for Multimedia Systems, pages 1–11, Oct. 2001.Google Scholar
  17. [17]
    M. Rabinovich, I. Rabinovich, R. Rajaraman, and A. Aggarwal. A dynamic object replication and migration protocol for an Internet hosting service. In 19th IEEE International Conference on Distributed Computing Systems (ICDCS’ 99), pages 101–113. IEEE, May 1999.Google Scholar
  18. [18]
    M. Rabinovich and O. Spatscheck. Web Caching and Replication. Addison-Wesley, 2001.Google Scholar
  19. [19]
    M. Rabinovich, Z. Xiao, F. Douglis, and C. Kalmanek. Moving edge-side includes to the real edge—the clients. In Proceedings of the 4th USENIX Symposium on Internet Technologies and Systems, Mar. 2003.Google Scholar
  20. [20]
    M. Sayal, Y. Breitbart, P. Scheuermann, and R. Vingralek. Selection algorithms for replicated web servers. In Workshop on Internet Server Performance, June 1998.Google Scholar
  21. [21]
    A. Wierzbicki. Models for Internet cache location. In The 7th Int’l Workshop on Web Content Caching and Distribution (WCW), 2002.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Michael Rabinovich
  • Zhen Xiao
  • Amit Aggarwal

There are no affiliations available

Personalised recommendations