Socially-aware pub-sub system for human networks

1. Socially-aware pub-sub system for human networks Yaxiong Zhao Jie Wu Department of Computer and Information Sciences Temple University Philadelphia 19122

2. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

3. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

4. Background: Why human networks? • Mobile wireless networks have been a dream • A lot of research • Ad hoc networks • Central of the past 20 years' research • Hardly hear any successful stories • People used to believe that mobile wireless networks should: • Support wireless internet • Be connected at all times • These are difficult and even impossible to realize

5. Background: Wireless networks that we did not build • Twitter: send messages to your followers and receive from people you are following • Very popular on mobile devices • Delay Tolerant networks • Intermittently connected mobile devices/hosts • How about combine them together? • A network formed by human carried wireless devices • Running social network applications • Do not require Internet-like infrastructure

6. Pub-sub for human networks • Pub-sub is a powerful paradigm • Publishers generate messages • Clients consume messages • Brokers forward messages according to their contents • The benefits of Pub-sub • Anonymity • Loose coupling • Flexibility • However, it requires complex processing on brokers and does not consider mobility • This paper tackles these problems

7. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

8. Overview • Two components • Content representation • Subscriptions and events • We use old classic methods in the literature • Pub-sub routing • Social election • Find socially-active users to forward messages

9. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

10. Traditional content representation • Subscriptions are represented as conjunctions of multiple attribute constraints • Each attribute has a constraint • Age = [10, 20], Height = [120, 190] • A subscription corresponds to a multi-dimensional region • An event is a multi-dimensional point • Excellent expressiveness • High processing and storage costs • Matching in multi-dimensional space is NP-hard in worst-case

11. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

12. Pub-sub routing • Brokers are responsible for forwarding messages • Who should be brokers? • This is not a problem for traditional pub-sub systems • However, in Human networks, it is difficult to find such users

13. Does DTN routing work? • The answer is, NO • It breaks the anonymity of pub-sub • Requires a lot of pre-processing • Impractical in practice • The obtained results do not hold for newly aquirred users in the network • It is difficult to obtain such data in the first place

14. Social election • Human networks are a social network • There will be active users moving around • How to find such users? • Election! • Each user should be in contact with a certain number of brokers • An interval [lower_bound, upper_bound] • If a user meets brokers less than or lower_bound • I may stay too far from the crowds • If the number is larger than upper_bound • I do not need so many brokers

15. Social election cont'd • Eventually, the most active users will become brokers • Since they move around in a larger area • They are more likely to become brokers

16. Social election cont'd • A heuristic based on popularity • The popularity of a user is measured as the number of different users it met in a time window [now – T, now] • This time window is the same as the one used in the election • The user should always select those of a higher popularity to be brokers

17. Pub-sub forwarding based on utility • A message's utility is defined as the division of the message's matching score and its age • An old message has less utility • The messages in a brokers buffer are ranked according to their utilities

18. Pub-sub forwarding cont'd • Forwarding happens only between brokers • Always forward highest-ranked messages • Buffer management • When the buffer is over-flowed • The lowest ranked messages will be purged from the buffer

19. Delegation forwarding • A utility threshold for each message • Forward it only when the next-hop has a better utility than its own threshold • The threshold raises after a successful forwarding • Reduce copy numbers

20. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

21. Experiment setting • Two mobility models • RWP and SLAW (mimic human mobility) • Written in C++ • 100 users in a 1000*1000m2 region • Communication range 50m • Compare with Random selection of brokers • A fraction of users are selected as brokers • The ratio is made to be the same as that obtained in our system

22. Delivery ratio RWP and delegation forwarding

23. Delivery ratio SLAW and delegation forwarding

24. Changing of brokers’ numbers with moving speed (RWP)

25. Outline • Background and motivation • Pub-sub system design • Subscription representation and processing • Pub-sub routing • Experiment results • Conclusion

26. Conclusion • Flooding in the entire network is too resource consuming • Finding a small set of brokers is sufficient for efficient message delivery

27. Questions? • Thanks for listening!