This talk is about “how we can exploit social information in content distribution systems”

1. Content Distribution based on Social Information Rubén Cuevas, Eva Jaho, Carmen Guerrero and Ioannis StavrakakisUniversity Carlos III MadridNational Kapodistrian University of Athens Paris, 16th October 2008

2. This talk is about “how we can exploit social information in content distribution systems”

3. Outline • Introduction • SwarmTella • OnMove • Conclusions

4. Introduction

5. Does this really make sense? • Success of Content Distribution Systems • P2P (Emule, BitTorrent), • APLICATION LAYER MULTICAST, • UGC Applications (YouTube) • Success of Social Applications • Instant Messaging (MSN) • Social Networks (FaceBook, LinkedIn,…) • People would use their social application for Content Distribution? • Yes  This makes sense • No  Give it up

6. How can we use social information in Content Distribution? • Identifying those users with similar social features • General • Similar profession • Similar hobbies • Similar interests • …. • Wireless Environments • Similar Mobility Pattern • And exchange contents with them

7. Which benefits can we obtain? • Accuracy  People Satisfaction • Targeted Content Advertisement • Retrieve contents that really fit my social profile • Cooperation • People collaborate if they get benefit from the system • Resource Saving • Avoiding flooding • Avoiding downloading not desired contents

8. Our contribution • We will present two systems that exploit Social Information in Content Distribution • SWARMTELLA (UC3M) • “Exploiting Social Information in P2P Content Distribution” • ONMOVE (UC3M and NKUA) • “Exploiting Social Information for Content Distribution in Wireless Delay Tolerant Environments”

9. SwarmTella

10. SwarmTella • General framework for distribution of different type of content (file-sharing, VoD Distribution and Live Streaming) • Community scenarios • It can be intended as a Recomendation System • Delivery techniques based on swarming • Nodes initially organized in an unstructured p2p • Distributed mechanism for building communities based on users common interests on contents: Ranking Algorithm

11. Ranking Algorithm • RA allows each node to identify other nodes with similar interest in a transparent way to the end user. • Each node generate a ranking of the other nodes. • Nodes with higher ranking means that have common interests to the local node. • It uses local information (light) • Received search queries • Swarm’s peers discovery

12. SPPiD • Secure Permanent Peer-ID (SPPiD) • The public part of a Public/Private key pair. • Transparent to the end user, generated and just used by the application • This allows to keep connection with other nodes along different sessions • Long term robust structure of the communities, long life of the IDs. • Privacity Concerns • Not Secure Permanent ID  KAD • User Ids  Skype • Mail Accounts  MSN, FaceBook

13. Swarmtella Publication Mechanism • .swarmtella file with metadata of the available content. • The node with a new content generates the .swarmetella file and an ADVERTISEMENT message to be sent to the a limited number of nodes (highest ranked) in the community.

14. Swarmtella Searching Mechanism • Multiattribute semantic query to the highest ranked nodes in the community • If it fails, then flooding algorithm in unstructured p2p (gnutella like)

15. BW consumed

16. Query Hit Rate

17. Top peers and community members content

18. SwarmTella • Next steps: • Design Details (e.g. Swarm Partition) • Real workload • Pattern of Encounters in Swarms • Uptime Pattern of P2P nodes • Plan  Crawling BT Swarms • TUDarmstadt and UC3M • Swarmtella Implementation • Validation in Controlled Environment • Emulab, ModelNet

19. OnMove

20. OnMove • A novel protocol for content distribution in wireless delay-tolerant environments • It is designed for handheld devices  mobile phones, PDAs, etc… • Multiple uses: • Advertisement Platform • UGC Distribution • Entertainment On the Road

21. A B university C t1 D A B cinema H G L t2 A G K concert M N O t3 DTN Scenario • Individual A may come in contact with individuals B, C and D in the morning for a duration of time t1. • Then she goes to the cinema and connects with other individuals for a duration of time t2. • In the evening she goes to the concert and meets other people for a duration of time t3.

22. A B university C t1 D A B cinema H G L t2 A G K concert M N O t3 DTN Scenario (cntd.) • A retrieves contents from B,C,D at the university • A stores them • A forwards the stored contents to B,G,H,L at the cinema • A forwards the stored contents to G,K,M,N,O at the concert

23. Social networking design of OnMove • Social networks can be either studied as: • whole networks with all of the ties describing relations in a defined population, or as • egocentric networksdescribingthe ties that one or more specific individuals have • OnMove is designed by considering egocentric or personal networks for each individual.

24. egocentric network of individual i i Egocentric Networks • Egocentric networks involve a focal individual (ego) and the individuals (alters) to which it is linked. • We study the exchange of data of the surrounding individual with the others in the group based on social interests. • Objectives: • To increase speed of content dissemination • To improve accuracy of content dissemination (align content dissemination with users’ interests)

25. A A B C D Content Exchange Procedure • When an individual comes in contact with other individuals in a social group (locality) • She exchanges its social profile with the others. • She has to decide from/to which node it is going to download/upload contents. • The individual ranks the others individuals in the locality • Download/Upload from/to highest ranked individual • The ranking algorithm is the core of the content exchange procedure, and should aim at increasing its effectiveness

26. Ranking parameters in OnMove • Social Similarity (SS): Similarity of social details (profession, interests, hobbies) of individuals • Content Accuracy (CA): Alignment of contents received by an individual from other individuals to his/her interests • Pattern of Meetings (PM): Defined by the frequency and the duration of these encounters • Connection Quality (CQ): Available bandwidth, interferences, type of connection (e.g., WiFi, Bluetooth)

27. D O C t1 B N A t3 H M K L G t2 Ranking parameters in OnMove (cntd.) • Egocentric Betweenness Bi of individual i: Number of pairs of neighbors of i that are not directly connected to each other. • Individuals with high value of egocentric betweenness have a lot of influence in the network as a lot of other individuals depend on them to make connections with other people. • Average Egocentric Betweenness (B*):

28. Ranking neighbours in OnMove Ranking metric for each individual: A weighted average of the previous parameters Weights for each parameter are assigned differently in different application scenarios

29. Application scenarios • Advertisement Platform • Objective: Maximize the dissemination of the advertised content (photo, video, etc.) • Relevant Parameters: B*, SS • File-Sharing on the Road: • Objective: Find contents of interest to a node • Relevant Parameters: SS, PM, CQ, CA

30. OnMove • Next steps: • Configuration and optimization of the ranking algorithm mechanism in several application scenarios. • Analyzing social profiles available on current systems such as FaceBook and exporting them to OnMove. • Evaluate OnMove in a real testbed. • Crawdad data (e.g. Haggle Project) • Analysis of OnMove in multihop networks

31. Content Distribution based on Social SwarmsRubén Cuevas, Eva Jaho, Carmen Guerrero and Ioannis StavrakakisUniversity Carlos III MadridNational Kapodistrian University Athens Paris, 16th October 2008