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Enabling Socially-Aware Distributed Systems or Some Ongoing Research in the Distributed Systems Group Adriana Iamnitchi anda@cse.usf.edu
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2 Thesis The wealth of social information exposed from multiple sources can be mined in the design of distributed computing infrastructures: to facilitate improved performance for traditional applications and services; to enable novel applications.
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3 Social Information Connects people through relationships Object centric: use of same objects Person centric: declared relationships or co- participation in events, groups, etc. Social relationships can be translated into: Trust Incentives for resource sharing Shared interest in content …
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4 Motivating Application: Socially-aware Call Censor
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5 Motivating Application: Personalized Evacuation Route
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6 Motivating Application: Data Placement
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7 “No 24 in B minor, BWV 869” “Les Bonbons” “ Yellow Submarine” “Les Bonbons” “Yellow Submarine” “Wood Is a Pleasant Thing to Think About” “Wood Is a Pleasant Thing to Think About” The interest-sharing graph G m T (V, E): V is set of users active during interval T An edge in E connects users who share at least m file requests within T An Example: Interest Sharing
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8 Small Worlds Word co-occurrences Film actors LANL coauthors Internet Web Food web Power grid D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks. Nature, 393:440-442, 1998 R. Albert and A.-L. Barabási, Statistical mechanics of complex networks, R. Modern Physics 74, 47 (2002).
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9 Web Interest-Sharing Graphs 7200s, 50files 3600s, 50files 1800s, 100files 1800s, 10file 300s, 1file
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10 DØ Interest-Sharing Graphs 7days, 1file 28 days, 1 file
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11 KaZaA Interest-Sharing Graphs 7day, 1file 28 days 1 file 2 hours 1 file 1 day 2 files 4h 2 files 12h 4 files
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12 Proactive Information Dissemination D0 Web Kazaa
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13 Objectives A service that can provide relevant social information to a variety of applications and services Maintains social information from unrestricted sources Exports a flexible interface for mining social knowledge allows user-controlled privacy protection
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14 State of the Art Application-limited collection and use of social information High bootstrap cost Limited (potentially inaccurate) information. E.g., Information from online social networks Hidden incentives to have many “friends” All relationships equal Symmetric relationships Newer proposals to merge different sources of social (and sensor) information Specifically targeting context awareness
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15 Prometheus: A P2P Social Data Management Service Collects social information from multiple sources (social sensors) Maintains this information in a social graph Offers a set of basic social inference functions
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16 Outline Motivation Prometheus architecture Social sensors Social graph representation Social data management in a Peer-to-Peer architecture Social inferences exported to applications and services Privacy protection Experimental results Performance under synthetic workloads CallCensor application
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17 Prometheus Peer-to-peer architecture Users contribute resources (peers) Fundamental change from typical peer-to-peer networks: not every user has its peer Input: Social information collected from different social sensors Applications running on behalf of users Collect information according to user-specified policies Report social information to Prometheus Output: Social information made available (subject to user policies) to applications and services
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18 Social Sensors A decentralized service that: Collects social information from multiple sources (social sensors) Maintains this information in a social graph Offers a set of basic social inference functions
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19 Social Sensors Location Collocation Schedule (e.g., Google calendar) Mobile phone activity (calls, sms) Online social network interactions Email Personal relations (family) Shared content Shared interest (e.g., CiteULike) …
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20 Social Sensors Report on behalf of ego: Alter, the person ego is interacting with An activity tag: e.g., “outdoors”, “dinning” Based on content, location, predefined labels, etc. A weight: e.g., 0.15 Run on ego’s mobile devices, desktops, web Aggregates and analyzes user interactions To reduce noise To distinguish between routine and meaningful interactions
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21 Social Sensors: Challenges Identifying activity tags: Mine text for keywords (emails, sms, blogs, etc) Reverse geo-coding to find where (co)located Predefined labels or dictionary and ontologies Quantifying interactions (assigning weights): Frequency, duration, time in-between interactions Familiar strangers versus active social interaction
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22 Prometheus Social Graph A decentralized service that: Collects social information from multiple sources (social sensors) Maintains this information in a social graph Offers a set of basic social inference functions
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23 Prometheus Social Graph
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24 Distributed Management of the Social Graph
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25 Prometheus Architecture
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26 Architecture Details Users have a unique user ID Select trusted peer group based on offline social trust with peer owners A user’s trusted peers communicate via Scribe Only the user’s trusted peers can decrypt user’s social data and thus perform social inference functions
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27 Social Data Protection 2 sets of public/private keys User’s User’s trusted peer group Social sensors submit data encrypted with the group’s public key and signed with the user’s private key Access to user’s private key only on user’s devices Data stored in the Pastry overlay Only trusted peers can decrypt and authenticate data
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28 Social Inference Functions A decentralized service that: Collects social information from multiple sources (social sensors) Maintains this information in a social graph Offers a set of basic social inference functions
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29 API for Applications: Social Inference Functions 5 basic social inference functions: relation_test (ego, alter, ɑ, w) top_relations (ego, ɑ, n) neighborhood (ego, ɑ, w, radius) proximity (ego, ɑ, w, radius, distance) social_strength (ego, alter) More complex functions can be built
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30 Social Strength Quantifies strength between ego and alter Result normalized to consider overall activity Search all paths of maximum 2 social hops One approach to quantify social strength. Others are certainly possible.
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31 Lessons from Experiments on PlanetLab Social-based mapping of users onto peers leads to significant performance gains: More than 15% of requests finish faster An order of magnitude fewer messages Availability through replication does not include high overhead Reasonable latency Code not optimized, significant performance gains compared to 2 weeks ago.
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32 Experimental Results: Neighborhood Requests
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33 Social Strength Request Results Similar performance with 2-hop Neighborhood Requests Search all 2-hop paths from source to destination
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34 Real Social Traces: NJIT Social Graph 100 randomly selected students from NJIT given Bluetooth-enabled phones that report their collocation Data recorded Collocation with two thresholds (45 and 90 minutes) Facebook friendships Sparse graph (commuters)
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35 CallCensor CallCensor implemented on Android Cell phone silenced, rings or vibrates depending on the social context and relationship with caller Relationship with caller: Social strength > threshold: allow call Caller connected by work and 1 hop away Caller connected by work and ≤ 2 hops away Real social data from 100 users stored on 3 nodes from PlanetLab Real time performance constraints
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36 CallCensor Results Met real-time performance constraint: response arrives before call forwarded automatically to voicemail
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37 Summary Prometheus: a decentralized service that enables socially-aware applications and services by collecting, managing and exposing social knowledge, subject to user- specified privacy policies. Unique contributions: Social graph representation Aggregated social data Social inference functions Socially-aware design
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38 Summary Users of Prometheus: Decide what personal social data are collected by installing/configuring social sensors Cooperate to store and manage their social data in a decentralized fashion Own and control access to their data Prometheus enables: Socially-aware applications that utilize social data collected from multiple sources Accurate social world representation through multi-edge, labeled, directed and weighted graph Improved performance through socially-aware P2P system design
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39 Future Work Improve Prometheus performance Network optimizations Caching of inference request results Develop new social sensors Develop new socially-aware applications & services Study tolerance to malicious attacks Exposure of social information to intermediate peers during request execution Manipulation of social connections to alter the structure of the social graph
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40 Why P2P? 1 st alternative: Free Centralized Service No incentives or business model for free storage and service of encrypted data 2 nd alternative: Cloud Cost for transferring and storing data Tradeoff between privacy & inference functionality 3 rd alternative: mobile phones Limited energy and computation power Not always online (service unavailability) Not always synchronized, for fast and efficient inference support
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41 Prometheus vs. Facebook? Both collect social information of users from multiple sources but: Facebook is limited to input from Facebook-controlled sources Prometheus accepts input from any user-defined social source (sensor) User-control of social information Prometheus allows full user-control: Storage of data Exposure of data to users, applications & services Facebook allows very limited user-control: Exposure of data to users, applications & services* Always at odds with its business model
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42 Updating the Social Graph Social data for each user stored as append-only file in P2P network Atomic appends using lock file for synchronization Trusted peers periodically check for new inputs for a user May have inconsistent data for short time periods Not major problem: social graphs do not change frequently After authentication, new input is merged with the social graph of the relevant user
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43 More Information Paper in Middleware 2010 (“Prometheus: User-Controlled P2P Social Data Management for Socially- Aware Applications”) http://www.cse.usf.edu/~anda anda@cse.usf.edu
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44 Acknowledgements My team of talented graduate students: National Science Foundation grants CNS-0831785 and CNS-0952420
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45 Thank you!
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46 Mobius Application Scenario: Community Multimedia Sharing (1) Alice Bob’s service Mobile tier P2P tier Register service Jane Mike Download mobile application for Bob’s service Service discovery service Alice, Mike & Jane friends Bob’s service enables mobile users to upload & share multimedia content Sharing community is specified according to type and strength of social ties
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47 Application Scenario: Community Multimedia Sharing (2) Alice Bob’s service Mobile tier Store Photo P2P tier Upload Photo Jane Jane’s PC Event notification service Mike Ad Hoc Collection of Jane’s Social Context Data Notify Alice’s Friends Event Notification Download Photo Service discovery service Service discovery
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48 P2P Tier Architecture Service 1 Service 2 Service n Service API EventManagerOffloadingAdmissionServiceDiscovery Network Privacy/Security Policy Enforcement Core Services for Mobile Tier Data Emergent Geo- Social Pattern Learning Service Geo-Social P2P Services Social State Geo-Socially Aware P2P Management Geo-Social Data Collection Overlay ContextProvider
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49 Application 1 Application 2 Application n Mobile API EventDispatcherResourceMonitor Ad Hoc Social Context OffloadingManagerLocationEngine Operating System Mobile Node Architecture
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