Download presentation
Presentation is loading. Please wait.
Published byAugustine Robinson Modified over 9 years ago
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
Similar presentations
© 2024 SlidePlayer.com. Inc.
All rights reserved.