Copyright © 2006, UCD Dublin Systems Research Group School of Computer Science and Informatics UCD Dublin, Belfield, Dublin 4, Ireland UCD Systems Research Group Like reality, but different… Scalable Information Dissemination for Pervasive Systems Graham Williamson Implementation and Evaluation

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?

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

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

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

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

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

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

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:

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:

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.

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

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

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

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

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 Next steps Finish installation of PlanetLab nodes Perform the evaluation

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

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

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

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