Social Cloud Computing


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Invited Talk at The Computation Institute, University of Chicago by Simon Caton

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  • Many service types: people servicesComputational servicesStorage servicesIn industry solutions this assumption is typically replaced by consumer proactive/reactive action – e.g. Amazon SLAOr they do not implement enforcement policiesThe problem:Anonymity between participants is commonE.g. Allocation through auctions or other market mechanismsThe models fall apart completely if this assumption is removed
  • Social Networks model relationshipsOpenSocial & OpenId, used by most social networking sites, andFacebook’s bespoke application framework
  • Economic model:Allows representation of User preferences as utility functionsCaptures things like costs to contribute as relative variables
  • Graph Nodes Publications EdgesBaseline 2335 1163 17973Double-Author 811 1163 5123Number of Authors 604 435 1988
  • Social Cloud Computing

    1. 1. Social Cloud ComputingSimon Caton SERVICE RESEARCH INSTITUTE (KSRI)KIT – University of the State of Baden-Württemberg andNational Research Center of the Helmholtz Association
    2. 2. 7 years of Cloud with and the same old hurdles Security Lack of Customisability Economics  Small scale consumers have ad hoc requirements  Providers have explicit incentives to lock in consumers  Countless attempts are yet to produce the open cloud market Trust  always assumed at some level  Anonymity (Market-based/broker allocation)  Many models fall apart when this is removed Karlsruhe Service Research Institute
    3. 3. Collaborative (Computing) Environments Users are lost in layer upon layer of abstraction Often make abhorrent trust assumptions  Certificates!: Represent little more than an underlying social relationship in a dehumanized format Massively specified – but limited in capability  Cannot see beyond the defined horizon  Usually single purpose (at most few purposes) Examples: Karlsruhe Service Research Institute
    4. 4. Social Networks Ubiquitous: Facebook surpassed 1 billion users Represent mostly pre-existing real world relationships Have notions of pre-existent trust fabric inherently interwoven into the network structure Many applications now use social networks as a platform for:  Authentication e.g. Facebook Connect  Online Presence e.g., Google Places  Application Portals e.g. progress thru processors, ASPEN and PolarGrid project Karlsruhe Service Research Institute
    5. 5. A Social Cloud Resources are idle 40-95% 1,000,000,000 Users On average 190 friends Users contribute to “good” causes Social Cloud: a resource, service and capability sharing framework utilizing relationships established between members of a social network Karlsruhe Service Research Institute
    6. 6. Talk Overview Vision of a Social Cloud Feasibility Study of a Social Market Platform Design Interaction: as a series of social and cognitive processes Use Cases: a Social CDN for academics, a social volunteer cloud Summary and Current Work6 Karlsruhe Service Research Institute
    7. 7. Social Cloud Vision Social Exchange Platform Social Cloud Karlsruhe Service Research Institute
    8. 8. Architecture: High Level Shared Access Ownership Social Tie “Resource” Infrastructure Server Social Middleware Karlsruhe Service Research Institute
    9. 9. Is a Social Cloud Feasible? From: Chard, Caton, Rana and Bubendorfer; Social Cloud: Cloud Computing in Social Networks; IEEE Cloud 2010 KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)9 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association
    10. 10. Architecture: Proof-of-concept Implementation Agreement10 Karlsruhe Service Research Institute
    11. 11. Feasibility Study: IEEE Cloud 2010 Can a Social Cloud Scale? What are the computational requirements for an avg. SN? Can a Social Cloud function in a timely manner as a Facebook application? Run on a single desktop11 Karlsruhe Service Research Institute
    12. 12. Constructing a Social Cloud Platform From: Haas, Caton, Chard and Weinhardt; Co-Operative Infrastructures: An Economic Model for Providing Infrastructures for Social Cloud Computing; HICSS 2013 KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)12 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association
    13. 13. How do we build a platform for a Social Cloud? Platform needs resources to:  Calculate allocations  Save bids and asks  Answer queries  …13 Karlsruhe Service Research Institute
    14. 14. Platform Design via Co-op Infrastructures A co-op is a scalable computing platform where all (computational) resources constituting the platforms infrastructure, as well as those made available over the platform, are owned and/or managed by its users. What contribution schemes can secure the resources are needed to keep a platform accessible?14 Karlsruhe Service Research Institute
    15. 15. Setting Platform Load: interpolated version of Cloud paper for a range of users [10, 400] Resource availability via SETI@home user distributions Users are modeled with varied compute resources Put these together and we “know” the total contribution needed Load is almost worst case:  every trade occurs nearly in parallel  half the social cloud takes part (if they meet min requirements)  we include simple levels of redundancy15 Karlsruhe Service Research Institute
    16. 16. Contribution Percentage 0.9 Availability Percentage 0.4 0.8 0.35 0.7 0.3 0.6Fixed Contribution 0.5 0.4 0.25 0.2 0.3 0.15 0.2 0.1 Idea: Users have to provide a given percentage of their available resources to 0.1 0.05 0 0 the co-operative infrastructure 400 10 20 50 100 200 10 20 50 100 200 400 Questions: Number of Users Number of Users rho_star do we set the percentage? How rho_star*1.1 rho_star*1.2 rho_star*1.5 rho_star rho_star*1.1 rho_star*1.2 rho_star*1.5a) System the effect of the contribution percentage on system reliability? of users, worst What is availability, worst case b) Average contributionrequirements case requirements 1 0.45 Contribution Percentage 0.9 Availability Percentage 0.4 0.8 0.35 0.7 0.3 0.6 0.5 0.25 0.4 0.2 0.3 0.15 0.2 0.1 0.1 0.05 0 0 10 20 50 100 200 400 10 20 50 100 200 400 Number of Users Number of Users rho_star rho_star*1.1 rho_star*1.2 rho_star*1.5 rho_star rho_star*1.1 rho_star*1.2 rho_star*1.5d) System availability, average e) Average contribution of users, Platform Availability Av. % Contributionrequirements average requirements16 Karlsruhe Service Research Institute
    17. 17. Voluntary Contribution Idea: Let users choose the amount of resources they contribute, based on individual preferences Approach:  User behavior modeled through Utility Functions with Other-Regarding Preferences (User Types: self-interested, altruist, hybrid)  Study dependence of system performance on the distribution of user types  Price variable to capture relative ease/difficulty to provide resources Platform Availability Av. % Contribution17 Karlsruhe Service Research Institute
    18. 18. Interaction in a Social Cloud as Social and Cognitive Processes From: Caton, Dukat, Grenz, Haas, Pfadenhauer and Weinhardt; Foundations of Trust: Contextualising Trust in Social Clouds; IEEE Social Computing and Its Applications 2012 KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)18 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association
    19. 19. Interaction: Social and Cognitive Processes Social Cloud Ex-Ante Social Ex-Post• Motivation: Outcome, Social Context, History Interchange • Feedback: Locally and to Network(s)• Excess driven demand • Formal Processes: • Recommendations: Rewards• Demand induced Social Initialisation, Identification, and Sanctions Capital Allocation, Provisioning • Interaction Archiving • Informal Social (History) Communication and Coordination Prior Completion Expectations Evolution of Relationship(s) 19 Karlsruhe Service Research Institute
    20. 20.  … an attribute of a relationship … in the necessary competence to be able to deliver … to deliver, keep promises etc.20 Participant Selection for an Experimental Social Cloud Karlsruhe Service Research Institute
    21. 21. What characterizes trust in collaboration? Observed, Within a specific recognized, scenario, setting history or understanding Trust is a proven contextualised product of dynamic social relationships that can be leveraged by formal and informal rules and conventions within a Social Cloud to facilitate as well as influence the scope of collaborative interchange. Implicit social Protocols, conventions polices etc.21 Participant Selection for an Experimental Social Cloud Karlsruhe Service Research Institute
    22. 22. Use Cases A Social CDN A Social Volunteer Cloud Chard, Caton, Rana and Katz; Under Seminar Paper: Dominik Ernst Review: DataCloud @ SC 2012 (KIT Undergrad) KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)22 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association
    23. 23. A Social Content Delivery Network forScientific Cooperation Replica Placement:  Random  Node Degree: highest no. of edges  Community Node Degree (highest degree within a community, i.e. no adjacent placement)  Clustering Coefficient (similar to highest betweenness scores) Karlsruhe Service Research Institute
    24. 24. Scenario and Community Representation Baseline Graph: DLBP publications graph (Kyle): 3 degrees (2009-10)  Nodes: authors, Edges: coauthorship of 1 or more papers Double coauthorship: at least 2 publications No. of Authors: < 6 authors on the paper Trust: captured through prior collaborative work Having constructed a network, we assign replicas, and then test with publications from 201124 Karlsruhe Service Research Institute
    25. 25. Results (at least 60 repetitions)Double Coauthorship No. of Coauthors 40 70 Random Random 35 Node Degree Node Degree 60 Community Node Degree Community Node Degree 30 Clustering Coefficient 50 Clustering Coefficient Replica Hit Rate (%) Replica Hit Rate (%) 25 40 20 30 15 20 10 5 10 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Number of Replicas Number of Replicas25 Karlsruhe Service Research Institute
    26. 26. Social Volunteer Cloud26 Karlsruhe Service Research Institute
    27. 27. Simulation-based study BOINC problem size: 2000 1500 Strangers (classic VC) vs. Social Cloud of 1500:  10 Friends, 20 Close Friends, 50 Friends, 50 Colleague, 120 Acquaintances, 250 Community Peers, 1000 FOFs Strangers follow SETI@home distributions Social Cloud SETI@home distributions + social constructs to improve reliability and availability proportional to closeness Scheduler is a simple FCFS + initial performance test27 Karlsruhe Service Research Institute
    28. 28. Results28 Karlsruhe Service Research Institute
    29. 29. Summary KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)29 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association
    30. 30. Summary A Social Cloud:  is an alternative to existing forms of distributed and collaborative computing  leverages existing social relationships to act as a means to establish a virtual compute cloud of excess/idle resources We’ve looked at:  Performance requirements of a Social Cloud  Methods to source the Platform via the Social Cloud  The Social and Cognitive Processes that underpin a Social Cloud  Some Use Cases: a Social CDN and Social BOINC Karlsruhe Service Research Institute
    31. 31. Research Areas and Challenges Policies Crowd Sourcing Socio-economicsSocial Markets Strategy Proof & Protocols Mechanisms Remedy Semantics Privacy Engineering31 Karlsruhe Service Research Institute
    32. 32. Thanks Come see us at eScience on Friday Oct 12th in the Application Systems and Frameworks Session: Haas, Caton, Trumpp, and Weinhardt; A Simulator for Social Exchanges and Collaborations - Architecture and Case Study KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI) Social Collaboration Simulator Monitoring User Incentive Experiment SmallWorldNetwork ... Application Sensor Applications Scheme Controller Exchange Exchange Sensor Mechanism Currency Artifact ... Mechanisms Runtime User Sensor Core User Resource Relationship TrustContext Elements Resource Sensor32 KIT – University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association