S-CUBE LP: Variability Modeling and QoS Analysis of Web Services Orchestrations

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S-CUBE LP: Variability Modeling and QoS Analysis of Web Services Orchestrations

  1. 1. S-Cube Learning PackageService Level Agreements:Variability Modeling and QoS Analysis of WebServices Orchestrations INRIA Sagar Sen, Benoit Baudry , Olivier Barais, www.s-cube-network.eu
  2. 2. Learning Package Categorization S-Cube SBA Quality Management Quality Assurance and Quality Prediction Variability Modeling and QoS Analysis of Web Services Orchestrations www.s-cube-network.eu
  3. 3. Learning Package Overview• Problem Description• Variability Modeling and QoS Analysis of Web Services Orchestrations• Discussion• Conclusions www.s-cube-network.eu
  4. 4. Feature Diagrams Feature Diagrams (FD) introduced by Kang et al. represent all configurations.[1] K. Kang, S. Cohen, J. Hess, W. Novak, and S.Peterson, “Feature-Oriented Domain Analysis (FODA)Feasibility Study,"Software Engineering Institute, 1990. www.s-cube-network.eu
  5. 5. Compatibility between FD and orchestrationsAn orchestration should invoke services corresponding to primitivenodes in a configuration (a valid instance of the FD). www.s-cube-network.eu
  6. 6. SLA in composite servicesExecution time for this car crash crisis management service? 6 www.s-cube-network.eu
  7. 7. SLA in composite servicesExecution time for this car crash crisis management service? 7 www.s-cube-network.eu
  8. 8. QoS models for atomic servicesCompute QoS distributions for atomic services 8 www.s-cube-network.eu
  9. 9. QoS models for atomic servicesCompute QoS distributions for atomic services 9 www.s-cube-network.eu
  10. 10. QoS for one configuraiton A DB E F MUX Merge 10 www.s-cube-network.eu
  11. 11. Large number of configurations Execution time forTotal number of this car crash possible crisis configurations: management 225 service? 11 www.s-cube-network.eu
  12. 12. Learning Package Overview• Problem Description• Variability Modeling and QoS Analysis of Web Services Orchestrations• Discussion• Conclusions www.s-cube-network.eu
  13. 13. ProposalAdapt pairwise selection to sample configurations in the composite serviceCompute QoS distributions for this sample 13 www.s-cube-network.eu
  14. 14. Motivating Questions• Generate configurations covering all pairwise interactions for a• composite service, ensuring variability is captured.• From this, infer variability in QoS parameters.• Stability with respect to the pairwise sample selected.• Comparison to exhaustive sampling of the configuration space. www.s-cube-network.eu
  15. 15. Methodology1. The modeling inputs may be specified as a 3- tuple (Services, Feature Diagram, Orchestration).2. Pairwise constraints are used to sample a set of configurations.3. QoS for orchestrations invoking services in the configuration.4. Comparisons with exhaustive sampling and consistency over multiple sample sets. www.s-cube-network.eu
  16. 16. Pairwise Samples•Combinatorial interaction testing (CIT) has been shown innetwork•monitoring case studies3 to reduce tests for 75 parameters with10^29 exhaustive combinations to only 28 tests.•CIT used to select a minimal set of configurations for fourboolean features A, B, C, D. • A Pairwise Sample consists of all configurations satisfying pairwise interactions for a composite service. • There can be many pairwise samples for a given FD (not unique). www.s-cube-network.eu
  17. 17. Explicit model of variability 17 www.s-cube-network.eu
  18. 18. Variability in the composite service 18 www.s-cube-network.eu
  19. 19. Pairwise test selection for Feature diagramA set TC of test configurations such that X1,…, Xn n features  i  [1..n] Xi  {0,1}  Xj, Xk |  Xja, Xkb |  c  TC | TC  Xja, Xkb  c  TC, c is a valid configuration w.r.t feature model www.s-cube-network.eu
  20. 20. Pairwise for composite services A Mandatory B C D Optional XOR E F Pairwise Interaction Configurations A¬B, A¬C, A¬D, A¬E, A¬F, ¬B¬D, ¬C¬D A AB, AC, BC, B¬D, B¬E, C¬D, C¬E, C¬F ABC AD, AE, C¬B, D¬B, E¬B, ¬B¬F, CD, CE, DE, E¬F ACDE B¬C, BD, BE, B¬F, D¬C, E¬C, ¬C¬F, D¬F ABDE AF, ¬B¬C, ¬B¬E, F¬B, ¬C¬E, F¬C, D¬E ADF BF, CF, DF, F¬E ABCDF www.s-cube-network.eu
  21. 21. Q1 ‘coverage’ of the pairwise sample 22 www.s-cube-network.eu
  22. 22. Q1 ‘coverage’ of the pairwise sample 23 www.s-cube-network.eu
  23. 23. Q2 pairwise vs. random 24 www.s-cube-network.eu
  24. 24. Q2 pairwise vs. random 25 www.s-cube-network.eu
  25. 25. Q3 stability of pairwisePercentile 25 25(max 50(min) 50(max 75(min 75(max 90(min 90(max (min) ) ) ) ) ) )Std. Dev. 2.18 1.52 2.59 1.73 2.90 1.82 3.19 1.83 26(seconds) www.s-cube-network.eu
  26. 26. Q4 establishing classes of SLA 27 www.s-cube-network.eu
  27. 27. Learning Package Overview• Problem Description• Variability Modeling and QoS Analysis of Web Services Orchestrations• Discussion• Conclusions www.s-cube-network.eu
  28. 28. Discussions• SLAs should take into account variable configurations and probabilistic nature of QoS parameters.• Product line of composite services with extensively analyzed SLAs.• Eliminating deviating configurations from SLAs.• Theoretical work to determine conditions when pairwise analysis can be used to sample QoS metrics. www.s-cube-network.eu
  29. 29. Learning Package Overview• Problem Description• Variability Modeling and QoS Analysis of Web Services Orchestrations• Discussion• Conclusions www.s-cube-network.eu
  30. 30. ConclusionPairwise is a systematic sampling techniqueInitial results for QoS prediction are encouragingAllows for a more realistic SLAs than current pessismistic (worst case) SLAs 31 www.s-cube-network.eu
  31. 31. Further S-Cube ReadingKattepur, S. Sen, B. Baudry, A. Benveniste, C. Jard, Variability Modeling and QoS Analysis of Web Services Orchestrations, In International Conference on Web Services, IEEE, 2010.Sagar Sen, Automatic Effective Model Discovery, PhD Thesis, Université de Rennes 1, June 2010 www.s-cube-network.eu
  32. 32. ReferencesA. Kattepur, S. Sen, B. Baudry, A. Benveniste, C. Jard, Pairwise Testing of Dynamic Composite Services, In International Symposium on Software Engineering for Adaptive and Self Managing Systems (SEAMS), IEEE, 2011.K. Kang, S. Cohen, J. Hess, W. Novak, and S. Peterson, “Feature-Oriented Domain Analysis (FODA) Feasibility Study," Software Engineering Institute, 1990.J. Misra and W. R. Cook, “Computation Orchestration: A Basis for Wide-area Computing,« Springer J. of Software and Systems Modeling, vol. 6, no. 1, pp. 83 – 110, Mar. 2007.D. M. Cohen, S. R. Dalal, J. Parelius, and G. C. Patton, “The Combinatorial Design Approach to Automatic Test Generation," IEEE Software, vol. 13, no. 5, pp. 83–88, Sept. 1996.J. Kienzle, N. Guelfi, and S. Mustafiz, “Crisis Management Systems: A Case Study for Aspect-Oriented Modeling," McGill Univ., Technical Report, 2009.G. Perrouin, S. Sen, J. Klein, B. Baudry, and Y. le Traon, “Automatic and Scalable T-wise Test Case Generation Strategies for Software Product Lines," Proc. of Intl. Conf. On Software Testing, April 2010.S. Rosario, A. Benveniste, S. Haar, and C. Jard, “Probabilistic QoS and Soft Contracts for Transaction-Based Web Services Orchestrations," IEEE Trans. on Services Computing, vol. 1, no. 4, pp. 187 – 200, 2008. www.s-cube-network.eu
  33. 33. Acknowledgements The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement 215483 (S-Cube). www.s-cube-network.eu

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