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Collaborative Task Execution In Volunteer Clouds (or how to choose a sub-reviewer)

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My talk at the 2nd General Meeting of the CINA project, Bologna, 18-20 Feb 2014.

The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google workload data.

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Collaborative Task Execution In Volunteer Clouds (or how to choose a sub-reviewer)

  1. 1. Collaborative Task Execution In Volunteer Clouds -- Michele Amoretti, PARMA -- Alberto Lluch Lafuente, IMT -- Stefano Sebastio, IMT 2nd General Meeting, Bologna, 18-20 Feb 2014
  2. 2. Collaborative Task Execution in Volunteer Clouds -- Michele Amoreti, PARMA -- Alberto Lluch Lafuente, IMT -- Stefano Sebastio, IMT
  3. 3. Collaborative Task Execution in Volunteer Clouds -- Michele Amoreti, PARMA -- Alberto Lluch Lafuente, IMT -- Stefano Sebastio, IMT
  4. 4. c ho o s e ow to cho ose H ow to H Collaborativeewer Execution in ub-reviiewer s ub-rev Task a s a Volunteer Clouds -- Michele Amoreti, PARMA -- Alberto Lluch Lafuente, IMT -- Stefano Sebastio, IMT
  5. 5. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2
  6. 6. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  7. 7. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  8. 8. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  9. 9. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  10. 10. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  11. 11. Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 X X X X X X X X X
  12. 12. ewers e revi hoos c domly t) ran lmos (a
  13. 13. SciFi Community
  14. 14. SciFi Community 1 Unstructured network
  15. 15. SciFi Community 1 Unstructured network 2 All members generate review tasks
  16. 16. SciFi Community 1 Unstructured network 2 All members generate review tasks 3 All members perform reviews
  17. 17. SciFi Community 1 Unstructured network 2 All members generate review tasks 3 All members perform reviews 4 Review requests may be forwarded
  18. 18. SciFi Community 1 Unstructured network 2 All members generate review tasks 3 All members perform reviews 4 Review requests may be forwarded 5 All members apply the same algorithm
  19. 19. SciFi Reviewers
  20. 20. SciFi Reviewers 1 No rescheduling, no priorities.
  21. 21. SciFi Reviewers 1 No rescheduling, no priorities. 2 Accept request iff CoS met.
  22. 22. SciFi Reviewers 1 No rescheduling, no priorities. 2 Accept request iff CoS met. 3 No delays.
  23. 23. SciFi Reviewers 1 No rescheduling, no priorities. 2 Accept request iff CoS met. 3 No delays. 4 Reply/Forward requests immediately.
  24. 24. SciFi Reviewers 1 No rescheduling, no priorities. 2 Accept request iff CoS met. 3 No delays. 4 Reply/Forward requests immediately. 5 Disclose confidence on research topics.
  25. 25. ALGORITHM 1: RANDOM
  26. 26. Algorithm 1: Random Each outgoing arc has the same probability of being chosen during request propagation.
  27. 27. Algorithm 1: Random Each outgoing arc has the same probability of being chosen during request propagation.
  28. 28. Algorithm 1: Random Each outgoing arc has the same probability of being chosen during request propagation.
  29. 29. Algorithm 1: Random
  30. 30. ALGORITHM 2: Greedy ORACLE
  31. 31. The Greedy ORACLE The oracle provides the sub-reviewer who will finish earlier.
  32. 32. The Greedy ORACLE The oracle provides the sub-reviewer who will finish earlier.
  33. 33. The Greedy ORACLE The oracle provides the sub-reviewer who will finish earlier.
  34. 34. ALGORITHM 3: FEEDBACK BASED
  35. 35. Probabilistic routing Arcs are labelled with rates to be used in probabilistic choices. 1 1+1 1 1 1 1
  36. 36. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 1 1+1 1 1 1 1
  37. 37. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 Can you review? * SE > 2 1 1+1 1 1 1 1
  38. 38. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 Can you review? * SE > 2 NO 1 1+1 1 1 1 1
  39. 39. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 Can you review? * SE > 2 NO 1 1+1 1 1 1 1-1
  40. 40. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 1 Can you review? 1 1+1 1 1 1 1-1
  41. 41. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 1 Can you review? Can you review? 1 1+1 1 1 1 1-1
  42. 42. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 1 Can you review? YES Can you review? 1 1+1 1 1 1 1-1
  43. 43. Feedback-based rates Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 1 Can you review? YES Can you review? 1+1 1+1 1 1 1 1-1
  44. 44. Feedback-based rates
  45. 45. Feedback-based rates
  46. 46. ALGORITHM 4: con+dence-based
  47. 47. Feedback-based pheromones Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 FM: 4 FM: 3 SE: 5 SE: 1 Arcs labeled with one rate for each research topic.
  48. 48. Feedback-based pheromones Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 FM: 4 FM: 3 SE: 5 SE: 1 FM: 3 SE: 1 Arcs labeled with one rate for each research topic.
  49. 49. Feedback-based pheromones Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 FM: 0 SE: 0 FM: 4 FM: 3 SE: 5 SE: 1 FM: 3 SE: 1 Arcs labeled with one rate for each research topic.
  50. 50. Feedback-based pheromones Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 FM: 0 SE: 0 FM: 4 SE: 5 FM: 4 FM: 3 SE: 5 SE: 1 FM: 3 SE: 1 Arcs labeled with one rate for each research topic.
  51. 51. Feedback-based pheromones Paper XXX Due dd/mm/yy CoS: * FM > 3 * SE > 2 FM: 0 SE: 0 FM: 4 SE: 5 FM: 4 SE: 5 FM: 4 FM: 3 SE: 5 SE: 1 FM: 3 SE: 1 Arcs labeled with one rate for each research topic.
  52. 52. Confidence-based Rates
  53. 53. Confidence-based Rates
  54. 54. Confidence-based Rates
  55. 55. ewers e revi hoos c domly t) ran lmos (a
  56. 56. What's next? 1 Study the impact of the structure of the overlay network
  57. 57. What's next? 1 Study the impact of the structure of the overlay network 2 Study reputation-based strategies
  58. 58. What's next? 1 Study the impact of the structure of the overlay network 2 Study reputation-based strategies 3 Application to routing of messages in predicate-based communication (cf. SCEL)
  59. 59. Questions?
  60. 60. References Work-in-progress partially reported in: “A Computational Field Framework for Collaborative Task Execution in Volunteer Clouds”, Stefano Sebastio, Michele Amoretti and Alberto Lluch-Lafuente, draft [PDF] “Reputation-based Cooperation in the Clouds”, Alessandro Celestini, Alberto Lluch Lafuente, Philip Mayer, Stefano Sebastio, and Francesco Tiezzi, draft [PDF] See also: The science cloud platform. http://svn.pst.ifi.lmu.de/trac/scp/. P. Mayer et al. The Autonomic Cloud: A Vision of Voluntary, Peer-2-Peer Cloud Computing, 3rd Workshop on Challenges for Achieving Self- Awareness in Autonomic Systems, 2013.

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