The document discusses the concept of a social cloud, which leverages existing social relationships to establish a virtual compute cloud using excess or idle resources. It outlines several key aspects: 1) Prior work that examined the performance requirements and feasibility of a social cloud. 2) Methods for building a platform to coordinate resource sharing via social contributions from users. 3) How interaction in a social cloud involves social and cognitive processes around trust and collaboration. 4) Two example use cases - a social CDN for academic content sharing and a social volunteer cloud for distributed computing projects.

1. Social Cloud Computing
Simon Caton http://www.facebook.com/SocialCloudComputing
http://www.ksri.kit.edu/SocialCloud
KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
KIT – University of the State of Baden-Württemberg and
National Research Center of the Helmholtz Association www.kit.edu

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
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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:
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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. fb.com/your_page, Google Places
 Application Portals e.g. progress thru processors, ASPEN and
PolarGrid project
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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
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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 Work
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7. Social Cloud Vision
Social
Exchange
Platform
Social Cloud
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8. Architecture: High Level
Shared Access
Ownership
Social Tie
“Resource”
Infrastructure
Server
Social
Middleware
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9. Is a Social Cloud Feasible?
From: Chard, Caton, Rana and Bubendorfer; Social Cloud: Cloud Computing in
Social Networks; IEEE Cloud 2010
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10. Architecture: Proof-of-concept Implementation
Agreement
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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 desktop
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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
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13. How do we build a platform for a Social Cloud?
Platform needs
resources to:
 Calculate allocations
 Save bids and asks
 Answer queries
 …
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14. Platform Design via Co-op Infrastructures
A co-op is a scalable computing platform where all
(computational) resources constituting the platform's
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?
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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 redundancy
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16. Contribution Percentage
0.9
Availability Percentage
0.4
0.8 0.35
0.7 0.3
0.6
Fixed 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*1.5
a) System the effect of the contribution percentage on system reliability? of users, worst
What is availability, worst case b) Average contribution
requirements 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*1.5
d) System availability, average e) Average contribution of users,
Platform Availability Av. % Contribution
requirements average requirements
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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. % Contribution
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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
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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)
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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
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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
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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)
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23. A Social Content Delivery Network for
Scientific 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)
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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 2011
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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 Replicas
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26. Social Volunteer Cloud
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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 test
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28. Results
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29. Summary
http://www.facebook.com/SocialCloudComputing
http://www.ksri.kit.edu/SocialCloud
KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
29 KIT – University of the State of Baden-Württemberg and
National Research Center of the Helmholtz Association www.kit.edu

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
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31. Research Areas and Challenges
Policies
Crowd Sourcing Socio-economics
Social Markets Strategy Proof
& Protocols Mechanisms
Remedy
Semantics
Privacy Engineering
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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 Sensor
32 KIT – University of the State of Baden-Württemberg and
National Research Center of the Helmholtz Association www.kit.edu