Scientific researchers faced with extremely large computations or the requirement of storing vast quantities of data have come to rely on distributed computational models like cloud computing. However, distributed computation is typically complex and expensive. The Social Cloud for Public eResearch aims to provide researchers with a platform to exploit social networks to reach out to users who would otherwise be unlikely to donate computational time for scientific and other research oriented projects. In this paper we explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through established social networks. We specifically look at integrating Facebook and BOINC, and discuss the architecture of the functional system and the novel social engineering algorithms that power it.

1. A Social Cloud for Public eResearch
Koshy John Kris Bubendorfer Kyle Chard
School of Engineering and School of Engineering and Computation Institute,
Computer Science Computer Science University of Chicago and
Victoria University of Wellington Victoria University of Wellington Argonne National Laboratory
Wellington, New Zealand Wellington, New Zealand Chicago, IL, USA

2. Public eResearch
• A cure for cancer? AIDS?
• Better cryptographic systems?
• Investigating climate change?
• Searching for intelligent life out there?
• What do they have in common?

3. eResearch with BOINC
• Berkeley Open Infrastructure for Network Computing
• Using BOINC for inexpensive computation
• Unused resources for worthy causes
• Making science accessible
• But is it achieving its potential?

4. BOINC Statistics
• BOINC projects have just 2.3 million users in total
• Only ~0.5 million are actively contributing
Facebook Statistics https://www.facebook.com/press/info.php?statistics
• On average, people on Facebook install apps more than 20
million times every day
• Every month, more than 500 million people use an app on
Facebook or experience Facebook Platform on other
websites
• More than 7 million apps and websites are integrated with
Facebook

5. But…
• BOINC marshals 5.5 petaflops from 0.5 million
Imagine if…
• Even 1% of Facebook’s user base joined & contributed actively
• That’s 8 million users vs. the current 0.5 million
• 90 petaflops?? – ballpark figure
At 10% and with hardware advances, we hit EXASCALE computing.
A viable solution to what Brian P. Schmidt asked of eScience??

6. What’s holding BOINC back?
• Lack of visibility
• Barriers to entry for new users
• Lack of ease in identifying and joining new projects
• Low visibility for new projects
• Low levels of active contribution

7. A Social Cloud
“A Social Cloud is a resource and service
sharing framework utilizing relationships and
policies established between members of a
social network.” – Kris Bubendorfer

8. A Social Cloud for Public eResearch
• A Facebook application
• Increasing awareness
• Making use of social relationships
• Truly bringing science to the people
• Potential for growth – going viral
• How is this better than just BOINC?
800
million

9. Deep Facebook Integration
• The Graph API – read/write
• The OpenGraph Protocol – entities as objects
• Social Channels
– News feed
– Requests
– Notifications

10. Social Engineering (part I)
• Easing the Process of Joining
– Interest Signature
– Project Signature
– Signature Distances (resource share)

11. Social Engineering (part II)
• Incentivising Involvement, Contribution & Growth
– Project Champions
• Total project credits
• Easier to become one of a less popular project
• Encourages contribution
• Champions the cause of a project – helps new users

12. Social Engineering (part II)
• Incentivising Involvement, Contribution & Growth
– Social Anchors
• Social Values and Social Scores
• To motivate users to encourage their friends to join
• Top bracket local to user’s social cloud

13. Social Engineering (part II)
• Incentivising Involvement, Contribution & Growth
– Compute Magnates
• Rolling credit value, compute value, compute score
• Social pressure on friends to contribute
• Top bracket local to user’s social cloud

14. Architecture

15. Interactions
Simple example of
interactions between all
actors associated with the
Social Cloud for Public
eResearch.

16. The Prototype

17. Results
• Verified interest signatures, project signatures
and signature distances through a user study.
• Studied the effects of project champions,
social anchors and compute magnates
through simulations on the IEEE VAST 2009
Challenge dataset.

18. Results – Signatures (small sample)

19. Results – Project Champions

20. Results – Social Anchors

21. Results – Compute Magnates

22. How is the Social Cloud better?
• GridRepublic
• Intel’s Progress Thru Processors

23. Conclusion
• Increased visibility and engagement
• More computational power for researchers
• Science becomes meaningful to the masses
• Brings lesser known projects into the limelight
• Science benefits. Humanity benefits.