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

5. “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

8. 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

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

14. 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.

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

16. 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.

19. Simulation results Path prediction algorithm Cache relocation scheme

23. 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.

24. 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).

25. Discussion