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Adaptive Applications for Wireless Information Technology

Adaptive Applications for Wireless Information Technology. Sujit Dey ECE Department University of California, San Diego dey@ece.ucsd.edu http://esdat.ucsd.edu. Ubiquitous Mobile Information Technology.

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Adaptive Applications for Wireless Information Technology

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  1. Adaptive Applications for Wireless Information Technology Sujit Dey ECE Department University of California, San Diego dey@ece.ucsd.edu http://esdat.ucsd.edu

  2. Ubiquitous Mobile Information Technology Heterogeneous Network Configurations, Access Technologies, Data Services, and Network Appliances

  3. Applications & Data Services for the Communication Era • Computing Era • Primary Resource: Computing • Paradox: Computing resources increased over years, but so did problem complexity • Solution:Efficient computing algorithms: minimize computation ops • Metrics: Runtime, space complexity • Communication Era • Primary Resource: Bandwidth • Paradox: As bandwidth capabilities increase, so will new data-intense wireless services and number of users • Solution: Adaptive Applications: minimize data traffic • Metrics:Quality of data; Quality of service

  4. Our Approach: Adaptive Wireless Data Wireless Gateway Server The Internet Base Station Access Profile Application Shaper Browser Network Conditions Application Requirements Configurable Handheld Client Client Characteristics Client Characteristics Web Servers

  5. Energy-Constrained Wireless Application Design : Motivation Enter. Headlines Fin. Headlines Basketball Summary Fin. Summary Enter. Summary Basketball Full Story Fin. Full Story Football Full Story Science Full Story Enter. Full Story END Start 0.3 0.15 0.1 0.1 0.1 0.2 0.05 Science Sports Enter. Finance Politics Breaking News 0.5 0.3 0.5 0.2 0.5 1 0.4 Politics Headlines 1 0.1 BN Headlines Basketball Headlines Football Headlines Science Headlines 0.8 0.9 1.0 0.4 0.2 0.9 Politics Summary 0.6 0.3 BN Summary Science Summary Football Summary 0.3 Politics Full Story 1.0 0.1 0.9 0.1 0.4 0.4 Weather

  6. Wireless Application Design : Energy Optimization Headlines Enter. Headlines Breaking News with Summary Fin. Headlines & Summary Basketball Summary Enter. Summary Basketball Full Story Science Full Story Fin. Full Story Football Full Story Enter. Full Story END Start & Weather 0.3 0.15 0.1 0.1 0.1 0.2 0.05 Science Sports Enter. Finance Politics 0.5 0.3 0.5 0.2 0.5 1 0.4 Politics Headlines Politics Headlines 0.1 Basketball Headlines Football Headlines 1.0 0.4 0.2 0.3 Politics Full Story Football Summary 0.3 Politics Full Story 1.0 0.1 0.9 0.1 0.4 0.4

  7. Wireless Applications : Energy Modeling & Optimization Goal : Develop energy modeling methodology for wireless application design exploration and optimize energy consumption Steps : • Develop an energy model for handheld clients (PalmVII) using measurement based methodology • Develop a generic energy estimation methodology for wireless applications

  8. Energy Model Time R T Encryption Decompression T * Enc R / Comp Connection Setup Decryption R * Enc / Comp Reception Transmission Idling Time L C0 E(R,T,C,L,Enc,Comp) = +Cidle*L + Ct*(T*Enc) + Cr*(C*R*Enc/Comp)+ + Cenc*T+Ccomp*R

  9. Measured Energy (J) % Error Application Estimated Energy (J) News 6.0 2.983 2.800 2.200 5.0 Travel 2.219 Personal 0.730 0.5 0.735 Validation of Energy Model • Comparison of measured energy consumption with energy estimation using the proposed model • Based on average energy consumption over 10 accesses Energy Model : Sufficiently Accurate to drive application level energy optimization

  10. Energy Aware Application Optimizations Migration Aggregation Start & Weather Headlines Politics Headlines Enter. Headlines Fin. Headlines Breaking News with Summary Fin. Headlines & Summary Basketball Summary Fin. Summary Enter. Summary Politics Full Story Basketball Full Story Cricket Full Story Science Full Story Fin. Full Story Enter. Full Story Splitting Deletion END Start 0.3 0.15 0.1 0.1 0.1 0.2 0.05 Science Sports Enter. Finance Politics Breaking News 0.5 0.3 0.5 0.2 0.5 1 0.4 Politics Headlines 1 0.1 BN Headlines Basketball Headlines Cricket Headlines Science Headlines 0.8 0.9 1.0 0.4 0.2 0.9 Politics Summary 0.6 0.3 BN Summary Science Summary Cricket Summary 0.3 Politics Full Story 1.0 0.1 0.9 0.1 0.4 0.4 Weather

  11. Exploration Methodology for Energy-Efficient Web Application Design Web Design Client Energy Model N1 N2 N4 N3 Web Page (b_tran, b_recv, priority) Access Probability N6 N5 Usage Profile Energy Estimator Probabilistic Web Flow Graph Transaction Generator

  12. Future Efforts Wireless Gateway Server The Internet Base Station Access Profile WLAN WCDMA Application Shaper Browser Network Conditions Application Requirements Configurable Handheld Client Client Characteristics Web Servers

  13. Network Aware Application Adaptation • Factors influencing network environment • Different network technologies (WLAN,WCDMA,HDR) • Time varying bandwidth inside a network technology • Example : Rate Adaptation in WCDMA/HDR • User Mobility • Network Load • Effect of the above factors on applications • Energy • Latency • Error Quality • Objective: Adapt applications according to the current network environment to minimize energy consumption, latency of communication, and error quality.

  14. Considering Network Effects on Energy • Extend energy model to incorporate network effects • simulation based • Network Parameters • Signal Strength • determines “transmission power level” • no of re-transmissions • Network Load • idle period • Energy Model Energy = TT(SS) * ( E(R,T,C,L,Enc,Comp) + TR(SS) ) + I(Network Load) TT(SS) & TR(SS) : Table lookups based on signal strength I (Network Load): Table lookup based on network load

  15. Test-bed Environment

  16. Conclusion • We developed an energy model and optimization techniques to adapt application to conserve energy • We are currently working to develop network aware adaptation techniques • We want to develop a test-bed environment to demonstrate and validate our ideas

  17. Network-Aware Application Adaptation • Idea : To deliver information to users transparent of network conditions • Means • Server : Re-organization of content • Techniques : Filtering, Splitting, Clustering • Step 1 : Filter out information which may not be useful • Step 2 : Split Information into atoms of information • Step 3 : Combine the atoms of information based on history information • Issues : • Temporary Storage of transformation • Maintenance of connection state information • Client : Pre-fetching & Presentation

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