1 / 17

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

jepling
Download Presentation

Evaluating Quality of Web Services: A Risk-driven Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related