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Using Proxy Cache Relocation to Accelerate Web Browsing in Wireless/Mobile Comm.
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Transcript of 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
Proceedings of The Tenth International World Wide Web Conference, May 1-5, 2001, Hong Kong.
Contents
Web caching in wireless environment “Moving” cache architecture Cache relocation scheme Path prediction algorithm Performance evaluation Simulation results Conclusion
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
“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
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
Cache relocation scheme
Move 100% to best guessed new BS, 70% to 2nd best guessed BS, 30% to other BSs.
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.
Path prediction algorithm
Main steps:– Receive prediction request.– Lookup matrix, send responses. – Receive feedback, update matrix
Matrix maintenance
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.
Simulation results
Path prediction algorithm Cache relocation scheme
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.
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).
Discussion