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Mobile Agents For Personalized Information Retrieval: When are they a good idea?

Ravi Jain and Farooq Anjum. Telcordia Contact: Farooq Anjum fanjum@telcordia.com. Mobile Agents For Personalized Information Retrieval: When are they a good idea?. Telcordia Technologies Proprietary – Internal Use Only

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Mobile Agents For Personalized Information Retrieval: When are they a good idea?

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  1. Ravi Jain and Farooq Anjum Telcordia Contact:Farooq Anjum fanjum@telcordia.com Mobile Agents For Personalized Information Retrieval: When are they a good idea? Telcordia Technologies Proprietary – Internal Use Only This document contains proprietary information that shall be distributed, routed or made available only within Telcordia Technologies, except with written permission of Telcordia Technologies. An SAIC Company

  2. Plan • Contributions • Mobile Agents • Performance Model • Performance Evaluation • Summary • Future Work

  3. Contributions • A preliminary analytical model for comparing performance benefits of using mobile agents over client server computing

  4. Client Server vs Mobile Agent

  5. Mobile Agents • Mobile Agent • Self contained piece of software that can migrate and execute on different machines in a dynamic networked environment • Justifications for using Mobile Agents • Performance benefits • reduction in network bandwidth consumption • reduced latency • reduced computation • increased fault tolerance • Software Engineering • conceptualize solutions better • improve code modularity and reusability

  6. Performance Model • A stationary user with a wireless last hop • Information gathering • Desire to consider the latency • with client server paradigm • with mobile agents • N servers to be searched for information • packets lost over wireless link with prob p • size of client query message is unity • size of agent is then M • parameter R to model information filtering by agents • At each site information found with probability pi and time to process query denoted ti

  7. System model for wireless information retrieval Can characterize the average information latency under the two paradigms

  8. Model Evaluation Scenarios • ALL • all N predetermined server sites are searched • SURE DECREASING • servers searched in decreasing order of success probability • SURE RANDOM • servers searched in random order • MAYBE-LARGE • information need not exist on servers searched • pi <1 • MAYBE-SMALL • information need not exist on servers searched • pi <0.1

  9. Visiting all servers without agent filtering • Agents advantageous for • small agent size without filtering

  10. Visiting all servers with agent filtering

  11. Visiting servers in sequential order With sequential search and no filtering advantage of mobile agents is lost

  12. Visiting servers in sequential order with filtering

  13. Visiting servers in random order If sites to be searched in random order then MA to be preferred

  14. Maybe Large Scenario A scenario that is not favorable to MA

  15. Maybe Small Scenario A scenario that is favorable to MA

  16. Summary • Developed a simple analytical model to quantify performance benefits of using mobile agent technology over client server techniques • Evaluated the model for different scenarios • Expected latency being the metric

  17. Future Work • Multiple Mobile Users • A detailed study • Queuing theoretic approach • Considering agent activation/inactivation

  18. Any questions

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