Multi-Objective Quality-Driven Service Selection—A Fully Polynomial Time Approximation Scheme

The goal of multi-objective quality-driven service selection (QDSS) is to find service selections for a workflow whose quality-of-service (QoS) values are Pareto-optimal. We consider multiple QoS attributes such as response time, cost, and reliability. A selection is Pareto-optimal if no other selection has better QoS values for some attributes and at least equivalent values for all others. Exact algorithms have been proposed that find all Pareto-optimal selections. They suffer however from exponential complexity. Randomized algorithms scale well but do not offer any formal guarantees on result precision. We present the first approximation scheme for QDSS. It aims at the sweet spot between exact and randomized algorithms: It combines polynomial complexity with formal result precision guarantees. A parameter allows to seamlessly trade result precision against efficiency. We formally analyze complexity and precision guarantees and experimentally compare our algorithm against exact and randomized approaches. Comparing with exact algorithms, our approximation scheme allows to reduce optimization time from hours to seconds. Its approximation error remains below 1.4 percent while randomized algorithms come close to the theoretical maximum.

[1]  Mihalis Yannakakis,et al.  Multiobjective query optimization , 2001, PODS '01.

[2]  D. Palanikkumar,et al.  Optimal Web Service Selection and Composition Using Multi-objective Bees Algorithm , 2011, 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications Workshops.

[3]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[4]  Harun Baraki,et al.  Heuristic Approaches for QoS-Based Service Selection , 2010, ICSOC.

[5]  Athman Bouguettaya,et al.  Computing Service Skylines over Sets of Services , 2010, 2010 IEEE International Conference on Web Services.

[6]  Francisco Curbera,et al.  Web Services Business Process Execution Language Version 2.0 , 2007 .

[7]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[8]  Enrique Alba,et al.  AbYSS: Adapting Scatter Search to Multiobjective Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[9]  Schahram Dustdar,et al.  Domain-Specific Service Selection for Composite Services , 2012, IEEE Transactions on Software Engineering.

[10]  Mike P. Papazoglou,et al.  Service oriented architectures: approaches, technologies and research issues , 2007, The VLDB Journal.

[11]  Piero A. Bonatti,et al.  On optimal service selection , 2005, WWW '05.

[12]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[13]  Athman Bouguettaya,et al.  Computing Service Skyline from Uncertain QoWS , 2010, IEEE Transactions on Services Computing.

[14]  Eyhab Al-Masri,et al.  Discovering the best web service , 2007, WWW '07.

[15]  L. Carvajal,et al.  IEEE Transactions on Software Engineering , 2016 .

[16]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[17]  Dorit S. Hochba,et al.  Approximation Algorithms for NP-Hard Problems , 1997, SIGA.

[18]  Athman Bouguettaya,et al.  Efficient Service Skyline Computation for Composite Service Selection , 2013, IEEE Transactions on Knowledge and Data Engineering.

[19]  Ralf Steinmetz,et al.  Heuristics for QoS-aware Web Service Composition , 2006, 2006 IEEE International Conference on Web Services (ICWS'06).

[20]  Kunal Verma,et al.  Constraint driven Web service composition in METEOR-S , 2004, IEEE International Conference onServices Computing, 2004. (SCC 2004). Proceedings. 2004.

[21]  Jiuxin Cao,et al.  Efficient Multi-objective Services Selection Algorithm Based on Particle Swarm Optimization , 2010, APSCC.

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

[23]  Toru Ishida,et al.  A Constraint-Based Approach to Horizontal Web Service Composition , 2006, International Semantic Web Conference.

[24]  Jin-Kao Hao,et al.  Selecting Web Services for Optimal Composition , 2005, SDWP@ICWS.

[25]  Yan Gao,et al.  Optimal Selection of Web Services for Composition Based on Interface-Matching and Weighted Multistage Graph , 2005, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05).

[26]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

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

[28]  Carlos A. Coello Coello,et al.  A new proposal for multi-objective optimization using differential evolution and rough sets theory , 2006, GECCO '06.

[29]  Fuyuki Ishikawa,et al.  Efficient QoS-Aware Service Composition with a Probabilistic Service Selection Policy , 2010, ICSOC.

[30]  Lifeng Ai,et al.  A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition , 2010, IEEE Congress on Evolutionary Computation.

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

[32]  Tao Yu,et al.  Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints , 2005, ICSOC.

[33]  Gero Mühl,et al.  QoS-Aware Composition of Web Services: An Evaluation of Selection Algorithms , 2005, OTM Conferences.

[34]  Huowang Chen,et al.  QoS-aware Service Composition Based on Tree-Coded Genetic Algorithm , 2007, 31st Annual International Computer Software and Applications Conference (COMPSAC 2007).

[35]  Lifeng Ai,et al.  A Penalty-Based Genetic Algorithm for QoS-Aware Web Service Composition with Inter-service Dependencies and Conflicts , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.

[36]  Luciano Baresi,et al.  Toward Open-World Software: Issue and Challenges , 2006, Computer.