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Using Proxy Cache Relocation to Accelerate Web Browsing in Wireless/Mobile Comm.

Using Proxy Cache Relocation to Accelerate Web Browsing in Wireless/Mobile Comm. Authors: Stathes Hadjiefthymiades and Lazaros Merakos Dept. of Informatics and Telecommunication – Uni. of Athens, Greece

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Using Proxy Cache Relocation to Accelerate Web Browsing in Wireless/Mobile Comm.

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  1. Using Proxy Cache Relocation to Accelerate Web Browsing in Wireless/Mobile Comm. Authors: Stathes Hadjiefthymiades and Lazaros Merakos Dept. of Informatics and Telecommunication – Uni. of Athens, Greece Proceedings of The Tenth International World Wide Web Conference, May 1-5, 2001, Hong Kong.

  2. Contents • Web caching in wireless environment • “Moving” cache architecture • Cache relocation scheme • Path prediction algorithm • Performance evaluation • Simulation results • Conclusion

  3. Web caching in wireless environment • MobiScape/Web Expess (1995/96) • Support Station (SS) is a gateway of Mobile Host (MH) • Both use proxy cache • Data is compressed • No changes in browsers, servers • SS must be reconstructed each time MH changes cell

  4. “Moving” cache architecture • Components: base stations (BS), mobile terminal, fixed terminal, routers. • Wireless cells: hexagonal shape, cover the entire surface. • User profiles: stored in home network, can be queried and forwarded using inter-network signaling. • Path prediction algo. : invoked after entering new cell, may be stored at home network

  5. Cache relocation scheme • A relocation process has these steps: • Determine_target[MT_ID,BS_ID]: MT to Home • Path prediction algorithm: Home • [MT_ID, Target_BSs, HO_Probabilities]: Home to MT • Cache compression: MT • MT_Cache[MT_ID,Cache]: MT- new BSs • Cache Decompression: New BSs • Handover: MT • Feedback[MT_ID,BS_ID]: New BS to Home • Clear_cache[MT_ID]: Home to unused BSs

  6. Cache relocation scheme • Move 100% to best guessed new BS, 70% to 2nd best guessed BS, 30% to other BSs.

  7. Path prediction algorithm • Based on learning automaton (an AI machine learning technique). • Learning automata: • Finite state adaptive systems that interact continuously with an environment. • Learn to adapt through a trial-error response process. • Input  Responses  Evaluate response  Feedback  Improve behavior. • Robust but not very efficient learners. Easy to implement.

  8. Path prediction algorithm • Main steps: • Receive prediction request. • Lookup matrix, send responses. • Receive feedback, update matrix • Matrix maintenance

  9. Performance evaluation setup • WWW traffic modeling: figure 9. • Cell residence time: time spent in current cell. This time is short if user travels very rapidly (in vehicle), it is long if user travels slowly (walking). • Path prediction programmed in Prolog. • Cache relocation scheme programmed in Visual C++. Metrics: avg. delay, # of interrupted connection, % of interrupted conn., hit rate, # of items used by MT in the new BS after handover.

  10. Simulation results • Path prediction algorithm • Cache relocation scheme

  11. Conclusion • Introduce a cache relocation and path prediction scheme for WWW browsing in wireless/mobile environment. • More robust learners in path prediction algorithm could be use.

  12. Comments • Relocating data: didn’t mention how second best guesses share data; how many second best guesses in general. • Path prediction: could be run from current BS without contacting home network. • Performance evaluation: didn’t compare with existing techniques. Didn’t study wasted bandwidth used for transfer data in incorrect predictions. • Contributions are not very clear since this technique adopts many things from existing techniques (architecture from MobileSpace, prediction algorithm from AI).

  13. Discussion

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