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26/04/2007BIS'07 Poznan, Poland1 Evaluating Quality of Web Services: A Risk-driven Approach Natallia Kokash Vincenzo DAndrea.

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Presentation on theme: "26/04/2007BIS'07 Poznan, Poland1 Evaluating Quality of Web Services: A Risk-driven Approach Natallia Kokash Vincenzo DAndrea."— Presentation transcript:

1 26/04/2007BIS'07 Poznan, Poland1 Evaluating Quality of Web Services: A Risk-driven Approach Natallia Kokash Vincenzo DAndrea

2 26/04/2007BIS'07 Poznan, Poland 2 Introduction Service-centric systems Quality of Service (QoS) Issues QoS-driven service selection Risk-driven service selection Risk analysis SOA risks Failure risk Experimental results Conclusions and Future Work Risk management for SOA References

3 26/04/2007BIS'07 Poznan, Poland 3 Service-centric systems s2s2 s1s1 + + s3s3 s5s5 + s4s4 + Client Provide r Partners s0s0 Invoke Sequential operator s 1 ; s 2 Parallel operator s 1 | s 2 Choice operator s 1 + s 2 Web service sisi Start state t0t0 End state t sisi + | Invoke

4 26/04/2007BIS'07 Poznan, Poland 4 Quality of Service Issues QoS for web services: Domain-independent Throughput, capacity, latency, response time (duration), availability, reliability, reputation, execution cost (price) Domain-dependent Currency converters: accuracy Hotel booking: prices, number of the rooms, availability rate How to: 1.specify QoS? 2.measure QoS? 3.specify user requirements and/or preferences about QoS? 4.match user requirements with existing services in terms of QoS? 5.rank services according to user preferences? 6.predict QoS factors under certain environmental conditions? 7.choose web services to guarantee certain QoS level of their composition?

5 26/04/2007BIS'07 Poznan, Poland 5 QoS-driven service selection Problems in quality-driven service selection: Lack of QoS statistics Volatility of QoS factors Multidimensionality Subjectivity Context-dependence Approaches Multi-attribute optimization [Ardagna and Pernici 2005, Zeng et al. 2004, Yu et al ] Constraints satisfaction [Martin-Diaz et al. 2005] Genetic algorithms [Canfora et al. 2006] Fuzzy [Lin et al. 2005] Problems with existing approaches Simplified models (e.g., one service for one task) Dependences among QoS factors are ignored Context is not taken into account

6 26/04/2007BIS'07 Poznan, Poland 6 Risk analysis Example: Movie: title= Rainmaker, format=DVD, languages=Italian, English Convert DVD to AVI: language=English SimpleDivX converter: time=2 hours, language = Italian Impact on time: 2 hours are lost Reason: Unexpected service behaviour (discrepancy with specification) Requires assessment of inherently uncertain events and circumstances Two dimensions: how likely the uncertainty is to occur (probability) what the effect would be if it happened (impact)

7 26/04/2007BIS'07 Poznan, Poland 7 SOA Risks Threats Loss of service, data, users Unexpected service behavior, changes Performance problems Contract violation Assessment Likelihoods and implications of threats Analysis of user expectations Service testing User feedback, reputation systems Mitigation Service selection, redundancy, redesign Runtime monitoring Contracts and policies

8 26/04/2007BIS'07 Poznan, Poland 8 Risk management for SOA

9 26/04/2007BIS'07 Poznan, Poland 9 Risk-driven service selection Loss function – defines the cost of service failure (money, time, resources) Choose the composition that maximizes the expected profit:

10 26/04/2007BIS'07 Poznan, Poland 10 Failure risk probability that some fault occurs resulting impact of this fault on the composite service where is the probability of the service failure. Loss function includes: Expenses to invoke failed service (its cost and response time) Service failure can cause rollback of the transaction, therefore expenses to execute precedent services are also included The provider may have to pay penalty to a user whose request was not accomplished.

11 26/04/2007BIS'07 Poznan, Poland 11 Failure risk of service compositions b-g b-e + + g-t e-t + g-e + b-g b-e + + g-t e-t + g-e +

12 26/04/2007BIS'07 Poznan, Poland 12 Failure risk: examples Success rate = 0.5; execution cost = 1; penalty = s1s1 s2s2 s1s1 s2s2 s3s3 + + s4s4 s2s2 s1s1 s3s3 + + s1s1 s3s3 + + s2s2 s4s4 ++ s1s1 s2s2 s3s3 + +

13 26/04/2007BIS'07 Poznan, Poland 13 Risk-driven selection algorithm Select an execution path with minimum risk value Notation: c – composition q(s i ) – quality parameter (response time, execution cost) p(s i ) – probability of success q max – resource limit Objective function: where

14 26/04/2007BIS'07 Poznan, Poland 14 Experimental results (1) Goal: Compare QoS of compositions chosen by our algorithm with QoS of compositions chosen by other methods Zeng et al. [2004] QoS factors: price, duration, reputation, success rate, availability Objective function: linear combination of scaled QoS factors Scaling: QoS factors range from 0 to 1 Weights reflect user preferences

15 26/04/2007BIS'07 Poznan, Poland 15 Experimental results (2) 100 simulated service compositions 10 services in each composition

16 26/04/2007BIS'07 Poznan, Poland 16 Conclusions and Future work A novel risk-based method for assessing QoS of web services is proposed Real world case studies Comparative analysis of existing service selection algorithms Risk management framework for automatic web service compositions Questions?

17 26/04/2007BIS'07 Poznan, Poland 17 References 1.[Ardagna and Pernici 2005] Ardagna, D., Pernici, B.: Global and Local QoS Constraints Guarantee in Web Service Selection, IEEE International Conference on Web Services, 2005, pp. 805– [Canfora et al. 2006] Canfora, G., di Penta, M., Esposito, R., Villani, M.-L.: QoS-Aware Replanning of Composite Web Services, Proceedings of the International Conference on Web Services, [Claro et al. 2005] Claro, D., Albers, P., Hao, J-K.: Selecting Web Services for Optimal Composition, Proceedings of the ICWS 2005 Second International Workshop on Semantic and Dynamic Web Processes, 2005, pp [Gao et al. 2006] Gao, A., Yang, D., Tang, Sh., Zhang, M.: QoS-driven Web Service Composition with Inter Service Conflicts, APWeb: 8th Asia-Pacific Web Conference, 2006, pp. 121 – [Lin et al. 2005] Lin, M., Xie, J., Guo, H., Wang, H.: Solving QoS-driven Web Service Dynamic Composition as Fuzzy Constraint Satisfaction, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2005, pp [Martin-Diaz et al. 2005] Martin-Diaz, O., Ruize-Cortes, A., Duran, A., Muller, C.: An Approach to Temporal-Aware Procurement of Web Services, International Conference on Service-Oriented Computing, 2005, pp. 170– [Zeng et al. 2004] Zeng, L., Benatallah, B., et al.: QoS-aware Middleware for Web Services Composition, IEEE Transactions on Software Engineering, Vol. 30, No. 5, 2004, pp. 311– [Yu et al. 2005] Yu, T., Lin, K.J.: Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints, International Conference on Service-Oriented Computing, 2005, pp. 130–143.


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