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PTC: Proxies that Transcode and Cache in Heterogeneous Web Client Environments

PTC: Proxies that Transcode and Cache in Heterogeneous Web Client Environments. Aameek Singh, Abhishek Trivedi, Krithi Ramamritham (IIT Bombay) AND Prashant Shenoy (University of Massachusetts, Amherst) Web Information Systems Engineering-02. Outline …. Introduction Transcoding Proxies

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PTC: Proxies that Transcode and Cache in Heterogeneous Web Client Environments

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  1. PTC: Proxies that Transcode and Cache in Heterogeneous Web Client Environments Aameek Singh, Abhishek Trivedi, KrithiRamamritham (IIT Bombay) AND Prashant Shenoy (University of Massachusetts, Amherst) Web Information Systems Engineering-02

  2. Outline … • Introduction • Transcoding Proxies • Caching Policies • Cache Replacement • Implementation and Results • Conclusions and Future Work

  3. Introduction • Diverse client devices • Differ in hardware, software and network connectivity • Each client requires specific content for efficient rendering • Different versions of same data • Image Resolution/Quality • Level of text summarization

  4. Transcoding • Large number of client-specific versions • Conversion of one data version to another • Decreasing Image Quality (JPEG quality level) - “convert” utility in Linux • Summarizing text- Copernicus Summarizer

  5. Possible Solutions • Server keeps all versions • Server transcodes on-the-fly • Intermediate Proxy Diversity! Resources! • More Control Appropriate! • Utility from an ISP

  6. Caching • Proxy Caching • New Issues • Multiple Versions of data • Transcoding possibilities in the cache • Cache Replacement

  7. Caching • Full Hit • Desired version available • Partial Hit • Higher version available • Secondary Hit • Lower version available • Miss • No version available

  8. Partial Hit • Two possibilities • Download desired version from server • Transcode higher version locally • Factors influencing decision • Transcoding Complexity • Proxy-server network connection • Load on proxy

  9. Proposed Strategy • Predict time to transcode (tAB) • Version before & after transcoding • Size of the object • Load on proxy • Predict time to download (tS) • Size of object • Network speed • Load on proxy

  10. Adaptive Policies • IP Based • Filter based on IP address of server • Min-Min • Assume next transfer to be quickest • Multiple Linear Regression • Predict based on a linear model • Complex Policies

  11. Proposed Strategy • Heuristic if M*tS < tAB then Download else Transcode locally • M – Level of conservativeness

  12. Secondary Hit • Proxy has a lower version • Need to integrate personalization • Profiling at proxy • Permanent Profiles • Session Characteristics • More utilities can be added

  13. Cache Replacement • Two kinds of utilities with each object • Reference utility • Transcoding utility • Cost savings important • WATCHMAN – Caching of Query Results

  14. Cache Replacement • Profit Metric of Oi – λc/s • λ - Average Reference Rate • c - Cost of obtaining Oi • s - Size of object • Extension • Include Transcoding utility • Add a term γb/s for each version, Oi can be transcoded into.

  15. Cache Replacement • Evaluation of parameters • λ = K/(t-tk) • c = min(ts, tVjVi), j = min(i+1,..n) • b = min(ts – tViVj, tVkVj – tViVj), k = min(i+1,..n) • Maintenance of parameters

  16. Related Work • Chang, Chen – ICDE 2002 • Weighted Transcoding Graphs • Constant Server Download Rate • Constant Transcoding Rate • Need Smarter policies

  17. Implementation and Results • Traces • 3000 JPG files • 5 concurrent clients with 3600 requests following Zipf’s Law (alpha=0.5) • Request stream consisting of 1200 Partial Hits • Local and Remote Source Servers

  18. Results • Local Source Server

  19. Local Source Server

  20. Results • Remote Source Server

  21. Remote Source Server

  22. Varying M

  23. Breakup of Performance • Local Source Server

  24. Breakup of Performance • Remote Source Server

  25. Processing Times

  26. Cache Replacement

  27. Different Request Ratios

  28. Conclusions and Future Work • Caching is essential and newer strategies are required • Adaptive policies are essential • Even simple policies will work • More policies and real-life client simulations are required • Scope of data objects should be increased

  29. Questions ?

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