Binary PSO for Web Service Location-Allocation

Web services are independently programmable application components which scatter over the Internet. Network latency is one of the major concerns of web service application. Thus, physical locations of web services and users should be taken into account for web service composition. In this paper, we propose a new solution based on the modified binary PSO-based (MBPSO) approach which employs an adaptive inertia technique to allocating web service locations. Although several heuristic approaches have been proposed for web service location-allocation, to our best knowledge, this is the first time applying PSO to solve the problem. A simulated experiment is done using the WS-DREAM dataset with five different complexities. To compare with genetic algorithm and original binary PSO approaches, the proposed MBPSO approach has advantages in most situations.

[1]  Sanjiva Weerawarana,et al.  Unraveling the Web services web: an introduction to SOAP, WSDL, and UDDI , 2002, IEEE Internet Computing.

[2]  M. A. Khanesar,et al.  A novel binary particle swarm optimization , 2007, 2007 Mediterranean Conference on Control & Automation.

[3]  Shinn-Ying Ho,et al.  OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Yue Ma,et al.  Quick convergence of genetic algorithm for QoS-driven web service selection , 2008, Comput. Networks.

[5]  Witold Pedrycz,et al.  Modified binary particle swarm optimization , 2008 .

[6]  Stefan Tai,et al.  The next step in Web services , 2003, CACM.

[7]  Xiaomeng Su,et al.  A Survey of Automated Web Service Composition Methods , 2004, SWSWPC.

[8]  Mrs Amandeep Kaur,et al.  AN OVERVIEW OF QUALITY OF SERVICE COMPUTER NETWORK , 2011 .

[9]  Thomas Erl,et al.  SOA Principles of Service Design , 2007 .

[10]  R. V. van Nieuwpoort,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 2003 .

[11]  Mohammad Saeed Jabalameli,et al.  An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem , 2012 .

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

[13]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[14]  Jesper M. Johansson.  On the impact of network latency on distributed systems design , 2000, Inf. Technol. Manag..

[15]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[17]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[18]  Lican Huang,et al.  Using Pareto Principle to Improve Efficiency for Selection of Qos Web Services , 2010, 2010 7th IEEE Consumer Communications and Networking Conference.

[19]  Yi Sun,et al.  A location-allocation problem for a web services provider in a competitive market , 2009, Eur. J. Oper. Res..

[20]  James Blondin,et al.  Particle Swarm Optimization: A Tutorial , 2009 .

[21]  Yi Sun,et al.  A location model for a web service intermediary , 2006, Decis. Support Syst..

[22]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[23]  A. Rezaee Jordehi,et al.  Particle swarm optimisation for discrete optimisation problems: a review , 2012, Artificial Intelligence Review.

[24]  Anne H. H. Ngu,et al.  QoS computation and policing in dynamic web service selection , 2004, WWW Alt. '04.

[25]  Mengjie Zhang,et al.  An Enhanced Genetic Algorithm for Web Service Location-Allocation , 2014, DEXA.

[26]  Zhenyu Liu,et al.  A Location & Time Related Web Service Distributed Selection Approach for Composition , 2010, 2010 Ninth International Conference on Grid and Cloud Computing.

[27]  Zibin Zheng,et al.  WSExpress: A QoS-aware Search Engine for Web Services , 2010, 2010 IEEE International Conference on Web Services.

[28]  Jamal Bentahar,et al.  A survey on trust and reputation models for Web services: Single, composite, and communities , 2015, Decis. Support Syst..

[29]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[30]  Xiaodong Li,et al.  An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization , 2016, IEEE Transactions on Evolutionary Computation.

[31]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[32]  Mehmet Sevkli,et al.  A discrete particle swarm optimization algorithm for uncapacitated facility location problem , 2008 .

[33]  Julian Togelius,et al.  Geometric particle swarm optimization , 2008 .

[34]  Tian Chao,et al.  On demand Web services-based business process composition , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[35]  Alejandro Zunino,et al.  Web Services Composition Mechanisms: A Review , 2015 .

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

[37]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Oliver Kramer,et al.  A Review of Constraint-Handling Techniques for Evolution Strategies , 2010, Appl. Comput. Intell. Soft Comput..

[39]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[40]  Maria Luisa Villani,et al.  A Lightweight Approach for QoS–Aware Service Composition , 2006 .

[41]  Zibin Zheng,et al.  Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation , 2016, IEEE Transactions on Services Computing.

[42]  Henning Schulzrinne,et al.  Internet Quality of Service: An Overview , 2000 .