eXO : Decentralized Autonomous Scalable Social Networking

1. eXO: Decentralized Autonomous Scalable Social Networking Andreas Loupasakis, Nikos NtarmosPeter Triantafillou CIDR 2011 Presented by: RujutaKamat

2. Motivation • Popularity of large scale social network applications running on centralized sites • Large scale user interactions, content sharing • Social network services force users to upload their content to a specific site in order to share it with others

3. Key concerns • Release of information to unintended users • Accidental data release • Intentional use of private data for marketing purposes • Release of ownership/control

4. Decentralized Social Networks People may be able to store their social network on their own serversand choose those servers to interact and synchronize automatically. One company doesn't own your data and social network anymore, you own and control it.

5. Autonomy • I control my data, I know who can access it, when or how it is replicated to which sites etc • “Our real social lives do not have central managers, and our virtual lives do not need them.”

6. Is it possible to architect, design, and implement decentralized social networking services that ensure scalability and efficiency? Can I retain full control of my content while also making it available to my friends for viewing, sharing, tagging?

8. Challenges • No central organization • Distributed indexing • Efficient storage and retrieval of meta-data • Efficient execution of distributed top-k queries • Exploiting user tags in a decentralized environment

9. Contributions • Distributed content indexing • Efficient Algorithms for ranked retrieval of distributed content • Tagging and exploiting tags to enrich search • Personal networks

10. Exo Architecture and Design • Built upon a peer to peer network of nodes, each of which runs a routing protocol for an overlay DHT network • Each user is associated with a unique network ID computed using a hash function which also serves as a node identifier

11. Distributed Hash Table (DHT)

12. Node roles • Request resolver • Storage interface for content and profile replicas • Storage of catalogues

13. Data Model • Content is indexed to appropriate network nodes • Content Profile • User Profile • Friend Lists

14. Example • User Profile- (t1, t2, t3…tn) - Rujuta ( UID, Rujuta, student, computer science, hobbies, interests) • Content Profile- (t1,t2,t3,….tn) – abc.jpg (filename, size, encoding, location)

15. Social Tags • User A tags the photo, abc.jpg owned by User B. Tagger (User A) Taggee (User B)

16. Autonomy and Privacy • Content stored only on user’s nodes and replicated on adjacent nodes for availability • Content can be marked as public or private • Public and private User profiles • Owner can reject access requests even for public content.

17. Catalogues • Each term ID is assigned to a node • Mapping of term ID to related user IDs along with a portion of user profile, content profiles

18. Indexing process • TID is computed for each term of the user/content profile • A message consisting of UID of source node, object profile, content owner’s profile is created and routed to the destination node • Catalogue entry stored at destination node • Each term processed in parallel

19. Query Model • Query Q= Qc + Qu • Qu: used to find user catalogue entries similar to queries. Return user’s profiles and indices of user’s source nodes “Find computer science students” • Qc: used to find catalogue entries of shared content objects “ Find all books authored by Jeffrey Archer”

20. Search and Ranking • Get query term TIDs • Local similarity matching at corresponding catalogue nodes • Q=Qc + Qu Content part and user part matched separately

21. Search and Ranking • Local top k catalogue results are returned by each catalogue node and global top-k results are found at query source node • Only one communication phase is needed to return results

22. Query enrichment with tagging • Retrieve tag cloud when you visit content owner’s node. • New search query can be formulated using the new set of terms discovered by the querying user

23. Personal Social Networks • Extra information supplied by user tagging can be utilized • Users can search for other similar users, experts in specific domains, professional acquaintances • Share information only with certain groups, direct queries to certain users

24. Experimental Results • Scalability is ensured given- --well balanced load among network nodes --small size of bandwidth consumption • User-perceived latency is low

25. Limitations and Open Issues • Content availability for private content • Meta-services • Anonymity, Autonomy and privacy • Distributed collaborative filtering

26. References • P. Druschel and A. Rowstron. Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems. In Proc. IFIP/ACM IFIP/ACM Intl. Conf. on Distributed Systems Platforms (Middleware), 2001. • J. Li, B. T. Loo, J. M. Hellerstein, M. F. Kaashoek, D. Karger, and R. Morris. On the feasibility of peer-to-peer web indexing and search. In Proc. Intl. Workshop on Peer-to-Peer Systems (IPTPS), 2003. • J. Li, B. T. Loo, J. M. Hellerstein, M. F. Kaashoek, D. Karger, and R. Morris. On the feasibility of peer-to-peer web indexing and search. In Proc. Intl. Workshop on Peer-to-Peer Systems (IPTPS), 2003. • A. Budura, S. Michel, P. Cudre-Mauroux, and K. Aberer. To tag or not to tag { harvesting adjacent metadata in large-scale tagging systems. In Proc. Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR), 2008