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Evaluating Quality of Web Services: A Risk-driven Approach

Evaluating Quality of Web Services: A Risk-driven Approach. Natallia Kokash Vincenzo D’Andrea. 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

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Evaluating Quality of Web Services: A Risk-driven Approach

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  1. Evaluating Quality of Web Services: A Risk-driven Approach Natallia Kokash Vincenzo D’Andrea BIS'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 BIS'07 Poznan, Poland

  3. s3 s1 + + s4 + s5 + s2 | + si Service-centric systems Partners Invoke Provider s0 Invoke Client BIS'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: • specify QoS? • measure QoS? • specify user requirements and/or preferences about QoS? • match user requirements with existing services in terms of QoS? • rank services according to user preferences? • predict QoS factors under certain environmental conditions? • choose web services to guarantee certain QoS level of their composition? BIS'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. 2005 ] • 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 BIS'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) BIS'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 BIS'07 Poznan, Poland

  8. Risk management for SOA BIS'07 Poznan, Poland

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

  10. Failure risk • probability that some fault occurs • resulting impact of this fault on the composite service where is theprobability 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. BIS'07 Poznan, Poland

  11. Failure risk of service compositions g-t b-g + + g-e + e-t + b-e g-t b-g + + g-e + e-t + b-e BIS'07 Poznan, Poland

  12. s1 s2 s1 s2 + + s3 s1 s2 + + s3 s4 s2 s1 + + s3 s1 s2 + + + + s3 s4 Failure risk: examples BIS'07 Poznan, Poland

  13. Risk-driven selection algorithm • Select an execution path with minimum risk value • Notation: • c – composition • q(si) – quality parameter (response time, execution cost) • p(si) –probability of success • qmax – resource limit • Objective function: where BIS'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 BIS'07 Poznan, Poland

  15. Experimental results (2) • 100 simulated service compositions • 10 services in each composition BIS'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? BIS'07 Poznan, Poland

  17. References • [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. 805–806. • [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, 2005. • [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. 32-45. • [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 – 132. • [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. 9-14. • [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. 170–184. • [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. 311–327. • [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. BIS'07 Poznan, Poland

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