Content Distribution based on Social Information

Rubén Cuevas, Eva Jaho, Carmen Guerrero and Ioannis StavrakakisUniversity Carlos III Madrid

National Kapodistrian University of Athens

Paris, 16th October 2008

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

systems”

Outline

Introduction SwarmTella OnMove Conclusions

Introduction

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

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

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

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”

SwarmTella

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

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

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

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.

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)

BW consumed

Query Hit Rate

Top peers and community members

content

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

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

OnMove

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.

t2

cinema

A B

GH

L

t3

concertA G

KMN

O

t1

university

C

B

D

A

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

t2

cinema

A B

GH

L

t3

concertA G

KMN

O

t1

university

C

B

D

A

Social networks can be either studied as:

whole networks with all of the ties describing relations in a defined population, or as

egocentric networks describing the ties that one or more specific individuals have

OnMove is designed by considering egocentric or personal networks for each individual.

Social networking design of OnMove

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)

Egocentric Networks

iegocentric network of individual i

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

C

B

D

AA

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)

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*):

t1

t2

t3

A

DC

B

H

LG

K

M

N

O

T

tii tB

TB

1

* )(1

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

**

)(

iBiCA

iCQiPMiSS

BwCAw

CQwPMwSSwiRank

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

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

Content Distribution based on Social Swarms

Rubén Cuevas, Eva Jaho, Rubén Cuevas, Eva Jaho, Carmen Guerrero and Ioannis StavrakakisUniversity Carlos III Madrid

National Kapodistrian University Athens

Paris, 16th October 2008