A Global QoS Optimizing Web Services Selection Algorithm Based on MOACO for Dynamic Web Service Composition

2009 ◽  
Author(s):  
Fang Qiqing ◽  
Peng Xiaoming ◽  
Liu Qinghua ◽  
Hu Yahui
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Web Service Composition ◽  
Selection Algorithm ◽  
Dynamic Web

scholarly journals Dynamic Web Service Composition through Network Analysis with Backtracking

2012 ◽  
pp. 40-45
Author(s):  
K. Rajeswari ◽  
E. Jeevitha ◽  
R. Stephy Graph ◽  
M. Suresh Kumar
Keyword(s):  
Network Analysis ◽  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Web Service Composition ◽  
New Paradigm ◽  
Hotel Room ◽  
Software Applications ◽  
Dynamic Web ◽  
Machine Interaction

A Web service is a software system designed to support interoperable machine-to-machine interaction over a network. Web services provide a standard means of interoperating between different software applications, running on a variety of platforms and/or frameworks [1]. One of the main advantages of the usage of web services is its ability to integrate with the other services through web service composition and realize the required functionality. This paper presents a new paradigm of dynamic web services composition using network analysis paired with backtracking in the perspective of tour planner where booking for flight, car, hotel room, scenic spots, etc are to be done. An algorithm called “Zeittafel” for the selection and scheduling of services that are to be composed is also presented.

Applying CBR Over an AI Planner for Dynamic Web Service Composition

2013 ◽  
Vol 8 (4) ◽  
pp. 36-47
Author(s):  
Fouad Henni ◽  
Baghdad Atmani
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Value Added ◽  
Research Area ◽  
Web Service Composition ◽  
Case Based Reasoning ◽  
Ai Planning ◽  
Childhood Immunization ◽  
Dynamic Web

Web services have emerged as a major technology for deploying automated interactions between distributed and heterogeneous applications. The main advantage of Web services composition is the possibility of creating value-added services by combining existing ones to achieve customized tasks. How to combine these services efficiently into an arrangement that is both functionally sound and architecturally realizable is a very challenging topic that has founded a significant research area within computer science. A great deal of recent Web-related research has concentrated on dynamic Web service composition. Most of proposed models for dynamic composition use semantic descriptions of Web services through the construction of domain ontology. In this paper, we present our approach to dynamically produce composite services. It is based on the use of two Artificial Intelligence (AI) techniques: Case-Based Reasoning (CBR) and AI planning. Our motivating scenario concerns a national system for the monitoring of childhood immunization.

QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

2021 ◽  
Vol 9 (2) ◽  
pp. 65-70
Author(s):  
Laishram Jenny Chanu ◽  
◽  
Arnab Paul ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Web Services ◽  
Web Service ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Web Service Composition ◽  
Swarm Optimization ◽  
Complex Query

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.

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