1. Copyright © 2006, UCD Dublin Systems Research Group School of Computer Science and Informatics UCD Dublin, Belfield, Dublin 4, Ireland http://www.csi.ucd.ie/ UCD Systems Research Group Like reality, but different… Scalable Information Dissemination for Pervasive Systems Graham Williamson Implementation and Evaluation

2. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 2 Introduction Middleware for pervasive systems Requirements  Easy for the application developer  Robust, resilient, scalable, self-organising…  Sensor information must be disseminated We propose “gossiping” for information dissemination  But is this a good solution?

3. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 3 Construct

4. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 4 Gossiping “Epidemic: An outbreak of a contagious disease that spreads rapidly and widely” (Dictionary.com) Gossiping is an epidemic algorithm:  Information spreads from a source through local interactions  People passing a disease ↔ Processes passing a message

5. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 5 Gossiping

6. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 6 Gossiping Key Points  Gossiping occurs in rounds  Rounds are not synchronised  Neighbours are chosen randomly  # of neighbours is called fanout Results in emergent behaviour -redundancy built in -purely local interactions  more scalable

7. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 7 Implementation

8. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 8 Implementation Protocols have two parts: 1. Sending process initiates gossips 2. Listening process waits for gossips Also, consider multiple messages:  Cannot infinitely buffer all messages  Gossip active message digests  Listener requests resends of missing messages

9. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 9 Implementation repeat forever { sleep ( T ); age_messages(); digest  message_history_summary(); repeat fanout times { peer  random_peer(); send ( digest, peer ); } Sending Process:

10. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 10 Implementation repeat forever { digest  wait_for_incoming_gossip(); my_digest  message_history_summary(); compare ( digest, my_digest ) { pull messages from sender and/or push messages to sender } Listening Process:

11. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 11 Evaluation – methodology We want to: Examine behaviour Facilitate comparisons Improve protocols Ultimately: Investigate the suitability of gossiping for this domain.

12. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 12 Evaluation – platforms Simulation with OmNet++  Modular and easy to work with Emulation using PlanetLab  Access to over 700 nodes  Fast, reliable network provides a baseline for other measurements

13. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 13 Evaluation – parameters Changing a parameter can have multiple effects  Reduce network load by increasing T?  Increase reliability by increasing fanout? An optimisation problem  Adaptive techniques for adjusting parameters at runtime

14. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 14 Evaluation – measurements What defines a good (or bad) gossiping protocol?  Low latency is good  But at what cost?  Data redundancy, good or bad? …Trade-offs

15. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 15 Evaluation – the vision Key Question Why do I have to write multiple implementations of the same algorithm???  Time consuming, error-prone… Ideal Solution  A single implementation for simulation, emulation, and deployment

16. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 16 Where are we now? Progress Initial identification of parameters and measurements Implementation in our middleware, Construct -see http://www.context-infrastructure.org Next steps Finish installation of PlanetLab nodes Perform the evaluation

17. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 17 Questions?

18. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 18 Security & Privacy Two main approaches:  Encryption of personal data such that it can only be read by trusted, privileged, parties  Not so appropriate for resource limited devices  Selective dissemination that ensures personal information is only gossiped with a trusted group of peers  Critical nodes *must* handle information regardless of trust

19. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 19 Parameters (detail)

20. Scalable Information Dissemination for Pervasive Systems: Implementation and Evaluation 20 Measurements (detail)