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A Framework for Composing Pervasive Applications

A Framework for Composing Pervasive Applications. Oleg Davidyuk , Ivan Sanchez, Jon Imanol Duran and Jukka Riekki. University of Oulu, Finland. Advances in Methods of Information and Communication Technology (AMICT'08) Workshop. plasma display. web/database servers. Resources.

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A Framework for Composing Pervasive Applications

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  1. A Framework for Composing Pervasive Applications Oleg Davidyuk, Ivan Sanchez, Jon Imanol Duran and Jukka Riekki University of Oulu, Finland Advances in Methods of Information and Communication Technology (AMICT'08) Workshop

  2. plasma display web/database servers • Resources • speaker system • projector mobile phone laptop The concept • Application • User

  3. speech text touch video gestures audio From: http://press.web.cern.ch/press/PressReleases/Releases2003/Images/ Potential Application Scenarios • Virtual Devices (or Resource Sharing) • Load Distribution (Grids and Web services) • Multimodal User Interfaces (speech, video kinetic, tactile)

  4. Composed pervasive applications S3 R2 S1 R3 S4 S2 R1 Service discovery Application Assembly Resource Management Ubiquitous middleware Conceptual Architecture Context providers

  5. Differences from the related work • Related work • Selecting resources according to a goal (COCOA) • Applications with proprietary architecture (Gaia) • Semantically independent systems (AURA, COCOA) • Our approach • Applications adapt their architecture • Support for applications regardless of their specific properties

  6. Application Model Platform Model Application and Platform Graphs • 30 nodes

  7. Increasing The objective function values Index Proposed Solution • Conclusions from the previous work: • The search problem is uncorrelated • Larger graphs  higher failure ratios • The algorithms: • Evolutionary (EA) and Genetic (GA) allocation algorithms • Hybrid problem handling (both CSP and OP) • Novel solution validation schema

  8. Comparison of the Algorithms • Evolutionary Algorithm • Simple implementation • Relies on random mutation operator • Genetic Algorithm • Complex (population handling, sorting, etc) • Uses guided genetic operators

  9. The fastest Performance Quality Most Stable Failure Ratio Difference 5~12% Analysis Logarithmic scale

  10. RFid tag RFid tag Practical Contribution • Installation: • Displays and media servers • 3 application components Remote UI

  11. Application setup time Average time, ms 79% 84% 91% 93% Number of resources in the enviroment The scalability test

  12. User experiments • Resources: 8 displays, 3 media servers • 10 users, STO’s students and research personnel • 100% of them wanted an additional control over the algorithm’s choices: • 70% wanted to confirm choices manually • 30% wanted to receive additional notifications • Users indicated usefulness of the approach, especially in public places • Many options to choose (many available resources) • Unfamiliar environment • 80% were satisfied with the algorithm’s choices (20% expected different results) • The usability was rated very high (9,5 out of 10 points)

  13. Future work • Increase the algorithm’s performance • Implement the next application scenario • Modify user interfaces and provide functionality required by the users • Study human-related aspects of application composition

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