A Service Selection Model to Improve Composition Reliability

1. A Service Selection Model to Improve Composition Reliability Natallia Kokash

2. Introduction • Web Service Composition Model • Quality of Service Issues • Related Work • Motivating Example • WS Selection Algorithm • Failure Risk • Reduction Rules • Sequence • Choice • Parallel • Conclusions and Future Work

3. Web Service Composition Model • Web service composition patterns: • Sequential • Parallel (and-split) • Synchronization (and-join) • Choice (or-split) • Merge (or-join) • Notation: • G = (T, S) – DAG, multiple edges between two nodes are permitted • T – set of states • S – set of available web services • t0 – start state • t – end state • si – web service • q(si) – set of quality parameters

4. Quality of Service Issues • Measurement and evaluation of QoS parameters for workflows: • Cardoso, J., Sheth, A., Miller, J., Arnold, J., Kochut, K.: "Quality of service for workflows and web service processes", Journal of Web Semantics, Vol. 1, No. 3, 2004, pp. 281--308. • QoS Ontolology: • Maximilien E.M., Singh M.P. A Framework and Ontology for Dynamic Web Services Selection, IEEE Internet Computing, No. 5, 2004.

5. Related Work • [Zeng 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. • [Ardagna 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. • [Yu 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. • [Claro 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. • [Canfora 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. • [Cao 2005] Cao, L., Li, M., Cao, J.: “Cost-Driven Web Service Selection Using Genetic Algorithm”, Workshop on Internet and Network Economics, 2005, pp. 906-915. • [Hacigumus 2005] Hacigumus, H, “Cost-Effective Service Composition”, WESC, in conjunction with ICSOC, 2005, pp. 93-100. • [Martin-Diaz 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. • [Bonatti 2005] Bonatti, P.A., Paola Festa, P.,: “On Optimal Service Selection”, Proceedings of the 14th international conference on World Wide Web, 2005,pp. 530-538. • [Gu 2003] Gu, X., Chang, R.: ”OoS-Assured Service Composition in managed Service Overlay Networks”, IEEE International Conference on Distributed Computing Systems, 2003. • [Lin 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. • [Gao 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.

6. [Zeng et al. 2004] Linear combination of: • price • duration • reputation • success rate • availability Scaling Weighting where Wj are user preferences

7. Questions • Scaling: • Use absolute values • Availability - 100% • 10% – 0.1 • 100% – 1 • Response time - timeout • Objective function: • Linear combination - ? • Service is cheap and fast but not available - ? • Should we rely on the preferences defined by a user? • Which service is better: • Cheap but unreliable, • Reliable but expensive? • A chosen service failed but the user is waiting for a result • Structure of a service composition graph

8. Motivating example • Goal: Translate a document from Belarusian to Turkish • Available web services: • Belarusian – English • Belarusian – German • German – Turkish • English – Turkish • German – English • Web service compositions that can satisfy the user’s goal: • Belarussian – English – Turkish • Belarussian – German – Turkish • Belarussian – German – English – Turkish

9. Dual Criteria Optimization • Notation: • c – composition • q(si) – quality parameter (response time, execution cost) • p(si) –probability of success • qmax – resource limit where • Time vs. cost: • The basic approach is to take the less important parameter as objective function provided that the most important criterion meets some requirements.

10. WS Selection Algorithm Failure risk – considers the probability that some fault will occur and the resulting impact of this fault on the composite service where is the probability of the service failure. Loss function – defines the cost of service failure (money, time, resources) • Algorithm: • For each state define failure risk of sub-compositions • Choose configuration with the minimal failure risk

11. Reduction Rules – Sequence Return state - either the start state or a state with out-degree > 1 i.e., failure risk = probability that the service will be invoked and will fail * loss function of this failure Probability of failure: Failure risk:

12. Reduction Rules – Choice i.e., failure risk = probability that the service will be invoked (all parallel services failed) and will fail * loss function of this failure Probability of failure: Failure risk:

13. Reduction Rules – Parallel • Centralized • If one of the branches failed the parallel one can be stopped immediately • Execution cost = the cost of the invoked services • Decentralized • Backward recovery • Forward recovery

14. Conclusions and Future Work • WS Selection algorithm • Estimates risks related to use of external services • Useful from a perspective of WS providers • Future Work • Experimental evaluation • More attention to parallel compositions and dependencies between services