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S-Cube Learning Package
Service Level Agreements:
Variability Modeling and QoS Analysis of Web Services Orchestrations
INRIA
Sagar Sen, Benoit Baudry , Olivier Barais,
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Learning Package Categorization
S-Cube
SBA Quality Management
Quality Assurance and Quality Prediction
Variability Modeling and QoS Analysis of Web Services Orchestrations
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Learning Package Overview
• Problem Description
• Variability Modeling and QoS Analysis of Web Services Orchestrations
• Discussion
• Conclusions
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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.
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Compatibility between FD and orchestrations
An orchestration should invoke services corresponding to primitive nodes in a configuration (a valid instance of the FD).
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SLA in composite services
Execution time for this car crash crisis management service?
6
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SLA in composite services
Execution time for this car crash crisis management service?
7
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QoS models for atomic services
Compute QoS distributions for atomic services
8
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QoS models for atomic services
Compute QoS distributions for atomic services
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QoS for one configuraiton
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A
B D
E F
MUX
Merge
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Large number of configurations
Total number of possible configurations: 225
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Execution time for this car crash crisis management service?
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Learning Package Overview
• Problem Description
• Variability Modeling and QoS Analysis of Web Services Orchestrations
• Discussion
• Conclusions
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Proposal
Adapt pairwise selection to sample configurations in the composite service
Compute QoS distributions for this sample
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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.
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Methodology
1. 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.
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Pairwise Samples
•Combinatorial interaction testing (CIT) has been shown in network
•monitoring case studies3 to reduce tests for 75 parameters with 10^29 exhaustive combinations to only 28 tests.
•CIT used to select a minimal set of configurations for four boolean 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).
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Explicit model of variability
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Variability in the composite service
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Pairwise test selection for Feature diagram
A 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
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Pairwise for composite services
Pairwise Interaction
A¬B, A¬C, A¬D, A¬E, A¬F, ¬B¬D, ¬C¬D
AB, AC, BC, B¬D, B¬E, C¬D, C¬E, C¬F
AD, AE, C¬B, D¬B, E¬B, ¬B¬F, CD, CE, DE, E¬F
B¬C, BD, BE, B¬F, D¬C, E¬C, ¬C¬F, D¬F
AF, ¬B¬C, ¬B¬E, F¬B, ¬C¬E, F¬C, D¬E
BF, CF, DF, F¬E
Configurations
A
ABC
ACDE
ABDE
ADF
ABCDF
A
B C D
E F
Mandatory
Optional
XOR
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Q1 ‘coverage’ of the pairwise sample
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Q1 ‘coverage’ of the pairwise sample
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Q2 pairwise vs. random
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Q2 pairwise vs. random
25
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Q3 stability of pairwise
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Percentile 25 (min)
25(max)
50(min) 50(max)
75(min)
75(max)
90(min)
90(max)
Std. Dev.
(seconds)
2.18 1.52 2.59 1.73 2.90 1.82 3.19 1.83
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Q4 establishing classes of SLA
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Learning Package Overview
• Problem Description
• Variability Modeling and QoS Analysis of Web Services Orchestrations
• Discussion
• Conclusions
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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.
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Learning Package Overview
• Problem Description
• Variability Modeling and QoS Analysis of Web Services Orchestrations
• Discussion
• Conclusions
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Conclusion
Pairwise is a systematic sampling technique
Initial results for QoS prediction are encouraging
Allows for a more realistic SLAs than current pessismistic (worst case) SLAs
31
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Further S-Cube Reading
Kattepur, 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
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References
A. 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.
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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).