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

www.s-cube-network.eu

S-Cube Learning Package

Service Level Agreements:

Variability Modeling and QoS Analysis of Web Services Orchestrations

INRIA

Sagar Sen, Benoit Baudry , Olivier Barais,


Learning Package Categorization

S-Cube

SBA Quality Management

Quality Assurance and Quality Prediction

Variability Modeling and QoS Analysis of Web Services Orchestrations


Learning Package Overview

• Problem Description

• Variability Modeling and QoS Analysis of Web Services Orchestrations

• Discussion

• Conclusions


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.


Compatibility between FD and orchestrations

An orchestration should invoke services corresponding to primitive nodes in a configuration (a valid instance of the FD).


SLA in composite services

Execution time for this car crash crisis management service?

6


SLA in composite services


7


QoS models for atomic services

Compute QoS distributions for atomic services

8


QoS models for atomic services

Compute QoS distributions for atomic services

9


QoS for one configuraiton

10

A

B D

E F

MUX

Merge


Large number of configurations

Total number of possible configurations: 225

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• Discussion

• Conclusions


Proposal

Adapt pairwise selection to sample configurations in the composite service

Compute QoS distributions for this sample

13


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.


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.


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).


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


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


Q1 ‘coverage’ of the pairwise sample

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Q1 ‘coverage’ of the pairwise sample

23


Q2 pairwise vs. random

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Q2 pairwise vs. random

25


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


Q4 establishing classes of SLA

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• Discussion

• Conclusions


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.





• Discussion

• Conclusions


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

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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


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.


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).

Technology

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