Towards robust service compositions in the context of functionally diverse services

Web service composition provides a means of customized and flexible integration of service functionalities. Quality-of-Service (QoS) optimization algorithms select services in order to adapt workflows to the non-functional requirements of the user. With increasing number of services in a workflow, previous approaches fail to achieve a sufficient reliability. Moreover, expensive ad-hoc replanning is required to deal with service failures. The major problem with such sequential application of planning and replanning is that it ignores the potential costs during the initial planning and they consequently are hidden from the decision maker. Our basic idea to overcome this substantial problem is to compute a QoS optimized selection of service clusters that includes a sufficient number of backup services for each service employed. To support the human decision maker in the service selection task, our approach considers the possible repair costs directly in the initial composition. On the basis of a multi-objective approach and using a suitable service selection interface, the decision maker can select compositions in line with his/her personal risk preferences.

[1]  Freddy Lécué,et al.  Optimizing Causal Link Based Web Service Composition , 2008, ECAI.

[2]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[3]  Valérie Issarny,et al.  EASY: Efficient semAntic Service discoverY in pervasive computing environments with QoS and context support , 2008, J. Syst. Softw..

[4]  Mike P. Papazoglou.  What's in a Service? , 2008, ICSOFT.

[5]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[6]  Anne H. H. Ngu,et al.  Declarative composition and peer-to-peer provisioning of dynamic Web services , 2002, Proceedings 18th International Conference on Data Engineering.

[7]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.

[8]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[9]  Jouni Lampinen,et al.  GDE3: the third evolution step of generalized differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[10]  C. Coello,et al.  Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

[11]  Fuyuki Ishikawa,et al.  A Probabilistic Approach to Service Selection with Conditional Contracts and Usage Patterns , 2009, ICSOC/ServiceWave.

[12]  Yanlong Zhai,et al.  An Efficient Approach for Service Process Reconfiguration in SOA with End-to-End QoS Constraints , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Carlos A. Coello Coello,et al.  Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[15]  Quan Z. Sheng,et al.  Quality driven web services composition , 2003, WWW '03.

[16]  M.C. Jaeger,et al.  Improving the QoS of WS compositions based on redundant services , 2005, International Conference on Next Generation Web Services Practices (NWeSP'05).

[17]  Junli Wang,et al.  Optimal Web Service Selection based on Multi-Objective Genetic Algorithm , 2008, 2008 International Symposium on Computational Intelligence and Design.

[18]  Junichi Suzuki,et al.  Multiobjective Optimization of SLA-Aware Service Composition , 2008, 2008 IEEE Congress on Services - Part I.

[19]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[20]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[21]  Valérie Issarny,et al.  QoS-Aware Service Composition in Dynamic Service Oriented Environments , 2009, Middleware.

[22]  Fuyuki Ishikawa,et al.  Applying QoS-Aware Service Selection on Functionally Diverse Services , 2011, ICSOC Workshops.

[23]  Mikko Laukkanen,et al.  Composing Workflows of Semantic Web Services , 2004 .

[24]  Takashi Kobayashi,et al.  A failure-aware model for estimating and analyzing the efficiency of Web services compositions , 2005, 11th Pacific Rim International Symposium on Dependable Computing (PRDC'05).

[25]  Fuyuki Ishikawa,et al.  QoS-Aware Automatic Service Composition by Applying Functional Clustering , 2011, 2011 IEEE International Conference on Web Services.

[26]  Daniel Kuhn,et al.  A Stochastic Programming Approach for QoS-Aware Service Composition , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[27]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[28]  Fuyuki Ishikawa,et al.  Bridging the Gap between Semantic Web Service Composition and Common Implementation Architectures , 2011, 2011 IEEE International Conference on Services Computing.