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Social Cloud: Cloud Computing in Social
               Networks



 Kyle Chard, Simon Caton, Omer Rana
         and Kris Bubendorfer
Emerging Themes


• Cloud Computing is growing in strength
   – Many providers e.g. Amazon EC2/S3, Google App
     Engine, Microsoft Azure and also many smaller
     scale open clouds such as Nimbus and Eucalyptus.


• Social Networking is increasingly ubiquitous:
   – E.g. Facebook has over 400 Million active users.
   – 50 % of these users log on every day
Current Cloud Scenarios and Problems


• Sharing
   – Finite capacity vs. fluctuating requirements
   – Many social peers with different capabilities
• Economy
   – Small scale consumers have ad hoc requirements
   – Money grabbing providers and inflexible lock-in
• Trust
   – always assumed at some level
   – Anonymity (Market-based/broker allocation)
   – Many models fall apart when this is removed
Social Networks


• Formed through pre-existing relationships,
   – i.e. your friends
• Have a pre-existent fabric of trust inherently
  interwoven into the network
   – How many of your friends do you not trust?
• Many applications now use social networks as a
  platform for:
   – Authentication e.g. Facebook Connect
   – Application Portals e.g. ASPEN and PolarGrid projects
• There already exist well established application
  APIs
The Social Cloud Vision




               +                       +


 The leveraging of pre-existing
 relationships in order to enable   A Social Cloud allows friends to share
 mutually beneficial interactions   capabilities within the context of a
 within a cloud context.            Social Network.
• An amalgamation of:
                                    Volunteer computing arises as users
   – Social Networking              can share resources for little or no
   – Cloud Computing                gain, perhaps through reciprocal
   – Volunteer Computing            arrangements.
Social Cloud Interaction Vision




                  Socially –
                  orientated
                   Market
                     Place



                     Social Cloud
Social Cloud Economy


 • Payment (in an economic sense) is optional
 • Instead we utilise a virtual currency
    – All collaborations involve a transfer of “credits”
    – All participants are given an initial amount of
      credits
    – No one can buy additional credits – they must be
      earned
    – Therefore, we can prevent free-riding, and actively
      encourage participation
Community Effect


• Susceptible to cheating through fabricated
  accounts
   – Social Enforcement: exclusion of anti-social peers
• To encapsulate the nature of an interaction an
  agreement is used for the domains:
   –   Technical Requirements
   –   Non-functional properties
   –   Temporal Requirements
   –   Economic preferences
• WS-Agreement + EJSDL + DRIVE API + Reservation
  + Social Cloud Extensions
Social Cloud Proof of Concept


• Simple Storage Service Implemented as a
  Facebook application
• Use Case: a back up facility




                           Agreement
Posted Price


  – Enables interactions based upon active trading/collaborative
    decisions
  – Intuitively facilitates reciprocal collaboration
  – Current “norm” in industry solutions

                                                  Social Cloud
                                                     MDS


  User ID URL Capacity   Price
  User1        100MB     5                                       Storage
                                        Storage
  User2        500MB     10                           Storage

  User3        5GB       7
Dynamic Auctions

• Auction:
   – Enables dynamic participant pairing
   – Sealed bid second price reverse auction
• Could be extended to any other auction mechanism
Evaluation


Research Questions:
• Can a Social Cloud Scale?
• What are the computational requirements for
  an “average” sized Social Cloud?
   – According to Facebook, the average social
     network has 130 participants
• Can a Social Cloud function in a timely manner
  as a Facebook application?
Posted Price Scalability


• Varying the size of the MDS and number of matches
• With a size of 2000, 100 matches can be discovered
  in ~ 2 seconds, which is reasonable
Auction Scalability

• 500 Auctions and the worst case scenario:
   – all auctions run concurrently
• Even with 50 bidders can still complete 65 auctions per
  minute
• Under our assumptions this is already enough for a large
  social network
Dissemination of Results


• A social (storage) cloud can be hosted using
  minimal resources (3 – 4 yr old PC)
• Components show good throughput under
  realistic loads
• However, scaling to millions of users would
  require a dedicated HPC or elastic
  environment
   – Co-op model  members sustain the platform
Conclusions & Future Work


• Social Cloud
   – Dynamic cloud environment leveraging existing trust
     relationships
   – Proof-of-concept: can be extended for many new
     scenarios
• Future Work
   – Computation, licenses and other capabilities
   – Combinatorial auctions
   – Generic scientific cloud communities – e.g.
     myExperiment
   – Evolution of the economic model
Questions?


Please look at our Prototype Social Cloud Video
http://www.im.uni-karlsruhe.de/SocialCloudDemo

Kyle@ecs.vuw.ac.nz / kyle@ci.uchicago.edu
Simon.Caton@kit.edu
O.F.Rana@cs.cardiff.ac.uk
Kris.Bubendorfer@ecs.vuw.ac.nz

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Social Cloud: Cloud Computing in Social Networks

  • 1. Social Cloud: Cloud Computing in Social Networks Kyle Chard, Simon Caton, Omer Rana and Kris Bubendorfer
  • 2. Emerging Themes • Cloud Computing is growing in strength – Many providers e.g. Amazon EC2/S3, Google App Engine, Microsoft Azure and also many smaller scale open clouds such as Nimbus and Eucalyptus. • Social Networking is increasingly ubiquitous: – E.g. Facebook has over 400 Million active users. – 50 % of these users log on every day
  • 3. Current Cloud Scenarios and Problems • Sharing – Finite capacity vs. fluctuating requirements – Many social peers with different capabilities • Economy – Small scale consumers have ad hoc requirements – Money grabbing providers and inflexible lock-in • Trust – always assumed at some level – Anonymity (Market-based/broker allocation) – Many models fall apart when this is removed
  • 4. Social Networks • Formed through pre-existing relationships, – i.e. your friends • Have a pre-existent fabric of trust inherently interwoven into the network – How many of your friends do you not trust? • Many applications now use social networks as a platform for: – Authentication e.g. Facebook Connect – Application Portals e.g. ASPEN and PolarGrid projects • There already exist well established application APIs
  • 5. The Social Cloud Vision + + The leveraging of pre-existing relationships in order to enable A Social Cloud allows friends to share mutually beneficial interactions capabilities within the context of a within a cloud context. Social Network. • An amalgamation of: Volunteer computing arises as users – Social Networking can share resources for little or no – Cloud Computing gain, perhaps through reciprocal – Volunteer Computing arrangements.
  • 6. Social Cloud Interaction Vision Socially – orientated Market Place Social Cloud
  • 7. Social Cloud Economy • Payment (in an economic sense) is optional • Instead we utilise a virtual currency – All collaborations involve a transfer of “credits” – All participants are given an initial amount of credits – No one can buy additional credits – they must be earned – Therefore, we can prevent free-riding, and actively encourage participation
  • 8. Community Effect • Susceptible to cheating through fabricated accounts – Social Enforcement: exclusion of anti-social peers • To encapsulate the nature of an interaction an agreement is used for the domains: – Technical Requirements – Non-functional properties – Temporal Requirements – Economic preferences • WS-Agreement + EJSDL + DRIVE API + Reservation + Social Cloud Extensions
  • 9. Social Cloud Proof of Concept • Simple Storage Service Implemented as a Facebook application • Use Case: a back up facility Agreement
  • 10. Posted Price – Enables interactions based upon active trading/collaborative decisions – Intuitively facilitates reciprocal collaboration – Current “norm” in industry solutions Social Cloud MDS User ID URL Capacity Price User1 100MB 5 Storage Storage User2 500MB 10 Storage User3 5GB 7
  • 11. Dynamic Auctions • Auction: – Enables dynamic participant pairing – Sealed bid second price reverse auction • Could be extended to any other auction mechanism
  • 12. Evaluation Research Questions: • Can a Social Cloud Scale? • What are the computational requirements for an “average” sized Social Cloud? – According to Facebook, the average social network has 130 participants • Can a Social Cloud function in a timely manner as a Facebook application?
  • 13. Posted Price Scalability • Varying the size of the MDS and number of matches • With a size of 2000, 100 matches can be discovered in ~ 2 seconds, which is reasonable
  • 14. Auction Scalability • 500 Auctions and the worst case scenario: – all auctions run concurrently • Even with 50 bidders can still complete 65 auctions per minute • Under our assumptions this is already enough for a large social network
  • 15. Dissemination of Results • A social (storage) cloud can be hosted using minimal resources (3 – 4 yr old PC) • Components show good throughput under realistic loads • However, scaling to millions of users would require a dedicated HPC or elastic environment – Co-op model  members sustain the platform
  • 16. Conclusions & Future Work • Social Cloud – Dynamic cloud environment leveraging existing trust relationships – Proof-of-concept: can be extended for many new scenarios • Future Work – Computation, licenses and other capabilities – Combinatorial auctions – Generic scientific cloud communities – e.g. myExperiment – Evolution of the economic model
  • 17. Questions? Please look at our Prototype Social Cloud Video http://www.im.uni-karlsruhe.de/SocialCloudDemo Kyle@ecs.vuw.ac.nz / kyle@ci.uchicago.edu Simon.Caton@kit.edu O.F.Rana@cs.cardiff.ac.uk Kris.Bubendorfer@ecs.vuw.ac.nz

Hinweis der Redaktion

  1. 400 million June 2010
  2. 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
  3. Social Networks model relationshipsOpenSocial & OpenId, used by most social networking sites, andFacebook’s bespoke application framework
  4. Any number of compensation or incentive mechanisms could be usedRemember that this is a social network, and cheating is anti-social behaviour in such a context. Therefore, we can assume that the network will aggressively respond to cheatingNote that here a SLA does NOT mean a contract, but an agreement between two parties
  5. Take it or leave it fixed price
  6. Attributes of a Second Price Sealed Bid auctionEncourages truth tellingLowers communication overhead
  7. Still 1 GB RAM
  8. Still with 1GB RAM, on an old machineStorage requests would imagine are long term – dynamic ad hoc usage may be a different story
  9. Thisimpl is a proof of concept there are so many ways we (or other people) can build upon this work....