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 –t 0 – start state –t – end state –s i – web service –q(s i ) – set of quality parameters

4. Quality of Service Issues ThroughputThe number of requests served in a given time period. CapacityA limit of concurrent requests for guaranteed performance. LatencyThe round-trip time between client request and service response. Response time (duration)The time taken by a service to process its sequence of activities. AvailabilityThe probability that a service is available. ReliabilityStability of a service functionality, i.e., ability of a service to perform its functions under stated conditions ReputationThe average rate of the service reported by clients. Execution cost (price)The amount of money for a single service execution. 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 1.[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. 2.[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. 3.[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. 4.[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. 5.[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. 6.[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. 7.[Hacigumus 2005] Hacigumus, H, “Cost-Effective Service Composition”, WESC, in conjunction with ICSOC, 2005, pp. 93-100. 8.[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. 9.[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. 10.[Gu 2003] Gu, X., Chang, R.: ”OoS-Assured Service Composition in managed Service Overlay Networks”, IEEE International Conference on Distributed Computing Systems, 2003. 11.[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. 12.[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] Scaling Weighting Linear combination of: –price –duration –reputation –success rate –availability where W j are user preferences

7. Questions 1.Scaling: Use absolute values Availability - 100% 10% – 0.1 100% – 1 Response time - timeout 2.Objective function: Linear combination - ? Service is cheap and fast but not available - ? 3.Should we rely on the preferences defined by a user? 4.Which service is better: 1.Cheap but unreliable, 2.Reliable but expensive? 5.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 where Notation: –c – composition –q(s i ) – quality parameter (response time, execution cost) –p(s i ) – probability of success –q max – resource limit 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 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 where is the probability of the service failure.

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