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Optimizing the Tradeoff between  Discovery, Composition, and    Execution Cost in Service         Composition             ...
Presentation Plan1. Introduction to Quality-Driven Service   Composition2. Tradeoff between Composition Effort and   Solut...
INTRODUCTION TO QUALITY-DRIVEN SERVICE COMPOSITION
Problem of Quality-Driven                          Service Composition                    Transcoding                     ...
Problem of Quality-Driven                          Service Composition                    TranscodingVideo                ...
Process in Quality-Driven          Service CompositionDiscovery        Optimization   Execution     Composition Phase
TRADEOFF BETWEEN COMPOSITION  EFFORT AND SOLUTION QUALITY
Tradeoff: Composition Effort vs.       Solution Quality   Optimize   Heavy load on               Middleware        Composi...
Tradeoff: Composition Effort vs.       Solution Quality      Composition Effort    Quality of the Solution
Tradeoff: Composition Effort vs.              Solution QualityC=             Composition Effort  CD            - Discovery...
Tradeoff: Composition Effort vs.              Solution QualityC=             Composition Effort  CD            - Discovery...
Dependency: Cost and #ServicesCost                CO                  CD                       #Services
Dependency: Cost and #ServicesCost          CE                CO                  CD                       #Services
Dependency: Cost and #ServicesCost                 C          CE                     CO                         CD        ...
ALGORITHM FOR AUTOMATICALLYTUNING COMPOSITION EFFORT
Sketch of Iterative AlgorithmRound i:     ∆CD,i                  ∆CO,i                       ∆CE,i  Discovery             ...
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      ?        ∆CD,i+...
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      =        ∆CD,i+...
Growth of Search Space for      Optimization            Search Space            Round i                      Search Space ...
Growth of Search Space for      Optimization                  Search Space                  Round i                       ...
Growth of Search Space for      Optimization                 Search Space                 Round i                         ...
Growth of Search Space for              Optimization (Cont.) t : number of tasks k: new services per task and iteration• S...
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      =        ∆CD,i+...
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      =        ∆CD,i+...
Ratio between Size of New and Old          Search Space Ratio diminishes, big improvements unlikely at some point
Diminishing ReturnsCost          CE                       #Iterations
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      =        ∆CD,i+...
Relation between Cost for Last Round       and Cost for New Round               Relation:       ∆CD,i      =        ∆CD,i+...
Sketch of Iterative AlgorithmRound i:     ∆CD,i                  ∆CO,i                       ∆CE,i  Discovery             ...
Sketch of Iterative AlgorithmRound i:     ∆CD,i                  ∆CO,i                   ∆CE,i  Discovery             Opti...
EXPERIMENTAL EVALUATION
Testbed Overview• Starting Point:   – Randomly generated sequential workflows with     randomly generated quality requirem...
Testbed Cost FunctionRepresent dynamic context by changing weights
Comparison: with vs. without Tuning                             10SPT   40SPT    70SPT     With Tuning                  80...
Comparison: with vs. without Tuning                             10SPT   40SPT    70SPT     With Tuning                  80...
Comparison: with vs. without Tuning                             10SPT   40SPT    70SPT     With Tuning                  80...
Comparison: with vs. without Tuning                             10SPT   40SPT    70SPT     With Tuning                  80...
CONCLUSION
Conclusion• Tradeoff between Composition Effort and  Solution Quality in Service Composition• Iterative Algorithm for Qual...
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Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition

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SOSOA talk presented at ICWS 2011.

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Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition

  1. 1. Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition Authors: Immanuel Trummer, Boi Faltings
  2. 2. Presentation Plan1. Introduction to Quality-Driven Service Composition2. Tradeoff between Composition Effort and Solution Quality3. Algorithm for Automatically Tuning Composition Effort4. Experimental Evaluation5. Conclusion
  3. 3. INTRODUCTION TO QUALITY-DRIVEN SERVICE COMPOSITION
  4. 4. Problem of Quality-Driven Service Composition Transcoding Invocation-Cost: 0.15$Video Response Time: 0.4 sec WS Candidates: - S1,1 Compression Merging WS - S1,2 WS … Candidates: Candidates: - S3,1 - S4,1 Translation - S3,2 - S4,2Text WS … … Candidates: - S2,1 - S2,2 …Example: «Web Services Selection for Distributed Composition of Multimedia Content», Wagner & Kellerer, 2004
  5. 5. Problem of Quality-Driven Service Composition TranscodingVideo WS Candidates: - S1,1 Compression Merging WS - S1,2 WS … Candidates: Candidates: - S3,1 - S4,1 Translation - S3,2 - S4,2Text WS … … Candidates: - S2,1 Goal: - S2,2 … - Cost < x $ per invocation - Minimize response timeExample: «Web Services Selection for Distributed Composition of Multimedia Content», Wagner & Kellerer, 2004
  6. 6. Process in Quality-Driven Service CompositionDiscovery Optimization Execution Composition Phase
  7. 7. TRADEOFF BETWEEN COMPOSITION EFFORT AND SOLUTION QUALITY
  8. 8. Tradeoff: Composition Effort vs. Solution Quality Optimize Heavy load on Middleware Composition Effort Tradeoff Adapt Dynamically! Quality of the Solution High-Priority Optimize Workflows
  9. 9. Tradeoff: Composition Effort vs. Solution Quality Composition Effort Quality of the Solution
  10. 10. Tradeoff: Composition Effort vs. Solution QualityC= Composition Effort CD - Discovery Cost+ CO - Optimization Cost Quality of the Solution+ CE - Execution Cost
  11. 11. Tradeoff: Composition Effort vs. Solution QualityC= Composition Effort CD - Discovery Cost+ CO - Optimization Cost Quality of the Solution+ CE - Execution Cost Parameter: #Downloaded Services per Task
  12. 12. Dependency: Cost and #ServicesCost CO CD #Services
  13. 13. Dependency: Cost and #ServicesCost CE CO CD #Services
  14. 14. Dependency: Cost and #ServicesCost C CE CO CD Minimum Cost #Services Where?
  15. 15. ALGORITHM FOR AUTOMATICALLYTUNING COMPOSITION EFFORT
  16. 16. Sketch of Iterative AlgorithmRound i: ∆CD,i ∆CO,i ∆CE,i Discovery Optimization next k services/task Within ? Execution current search space Condition for Next Iteration?
  17. 17. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i ? ∆CD,i+1 ∆CO,i ? ∆CO,i+1 ∆CE,i ? ∆CE,i+1
  18. 18. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i = ∆CD,i+1 ∆CO,i ? ∆CO,i+1 ∆CE,i ? ∆CE,i+1
  19. 19. Growth of Search Space for Optimization Search Space Round i Search Space Round i+1
  20. 20. Growth of Search Space for Optimization Search Space Round i Search Space Round i+1 Explored by Inefficient Method in Round i+1
  21. 21. Growth of Search Space for Optimization Search Space Round i Search Space Round i+1 Explored by Efficient Method in Round i+1
  22. 22. Growth of Search Space for Optimization (Cont.) t : number of tasks k: new services per task and iteration• Search Space Size in round i:• Search Space Size in round i+1:• Size of newly added search space: Size of newly added search space grows from round to round
  23. 23. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i = ∆CD,i+1 ∆CO,i ? ∆CO,i+1 ∆CE,i ? ∆CE,i+1
  24. 24. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i = ∆CD,i+1 ∆CO,i ∆CO,i+1 ∆CE,i ? ∆CE,i+1
  25. 25. Ratio between Size of New and Old Search Space Ratio diminishes, big improvements unlikely at some point
  26. 26. Diminishing ReturnsCost CE #Iterations
  27. 27. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i = ∆CD,i+1 ∆CO,i ∆CO,i+1 ∆CE,i ? ∆CE,i+1
  28. 28. Relation between Cost for Last Round and Cost for New Round Relation: ∆CD,i = ∆CD,i+1 ∆CO,i ∆CO,i+1 ∆CE,i ∆CE,i+1
  29. 29. Sketch of Iterative AlgorithmRound i: ∆CD,i ∆CO,i ∆CE,i Discovery Optimization next k services/task Within ? Execution current search space Condition for Next Iteration?
  30. 30. Sketch of Iterative AlgorithmRound i: ∆CD,i ∆CO,i ∆CE,i Discovery Optimization next k services/task Within ? Execution current search space Number of iterations is near-optimal
  31. 31. EXPERIMENTAL EVALUATION
  32. 32. Testbed Overview• Starting Point: – Randomly generated sequential workflows with randomly generated quality requirements• Discovery: – Randomly generated service candidates – Simulated registry download• Optimization: – Transformation to Integer Linear Programming problem – Use of IBM CPLEX v12.1• Verified that our initial assumptions hold
  33. 33. Testbed Cost FunctionRepresent dynamic context by changing weights
  34. 34. Comparison: with vs. without Tuning 10SPT 40SPT 70SPT With Tuning 800% 700% 600%Aggregated Cost 500% 400% 300% 200% 100% 0% doe Doe dOe doE DoE dOE DOe Scenario
  35. 35. Comparison: with vs. without Tuning 10SPT 40SPT 70SPT With Tuning 800% 700% 600%Aggregated Cost 500% 400% 300% 200% 100% 0% doe Doe dOe doE DoE dOE DOe Scenario
  36. 36. Comparison: with vs. without Tuning 10SPT 40SPT 70SPT With Tuning 800% 700% 600%Aggregated Cost 500% 400% 300% 200% 100% 0% doe Doe dOe doE DoE dOE DOe Scenario
  37. 37. Comparison: with vs. without Tuning 10SPT 40SPT 70SPT With Tuning 800% 700% 600%Aggregated Cost 500% 400% 300% 200% 100% 0% doe Doe dOe doE DoE dOE DOe Scenario
  38. 38. CONCLUSION
  39. 39. Conclusion• Tradeoff between Composition Effort and Solution Quality in Service Composition• Iterative Algorithm for Quality-Driven Service Composition• Tuning of Composition Effort  Gains in Efficiency• Iterative scheme is genericImmanuel.Trummer@epfl.ch

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