A fault-tolerant framework for QoS-aware web service composition via case-based reasoning

2014 ◽  
Vol 10 (1) ◽  
pp. 80 ◽  
Author(s):  
Guoqiang Li ◽  
Lejian Liao ◽  
Dandan Song ◽  
Zibin Zheng
Author(s):  
Chiung-Hon Leon Lee ◽  
◽  
Alan Liu ◽  
Huan-Hsian Huang ◽  

Planning commonly applied to automating Web Service composition involves two problems - (i) overlooked user needs combined with services provided by the systems themselves and outside services providing a much more flexible service model. (ii) “Speeding up” and “facilitating” services by not recording information about service providers already having served users and about planning already processed. We propose merging internal and external services to meet user needs. Internal services include system functions designed to meet user needs. External services mean Web services provided by outside service providers. We plan to combine both types of services to create planning to meet user needs. We apply case-based reasoning to store planning and related information in a case base to make planning much faster when users have similar needs.


Author(s):  
Yudith Cardinale ◽  
Joyce El Haddad ◽  
Maude Manouvrier ◽  
Marta Rukoz

Web Service (WS) composition consists in combining several WSs into a Composite WS (CWS), which becomes a value-added process. In order to provide reliable and fault-tolerant CWSs, several transactional-aware composition approaches have been proposed. However, as far as we know, no real classification survey of such approaches exists. This is the contribution of this chapter. Our classification distinguishes the more relevant and recent propositions in two groups: approaches based on WS transactional properties and the ones also integrating QoS criteria to the composition process. All these studied approaches are compared according to several criteria: the transactional model used or proposed, the control flow model used or automatically generated, the mechanism proposed to verify the transactional property of the composition, the step(s) of the composition process involved in, and the protocols or the standard languages used or extended. This classification allows underlining the lacks and the future directions which should be studied.


2011 ◽  
pp. 604-622
Author(s):  
Taha Osman ◽  
Dhavalkumar Thakker ◽  
David Al-Dabass

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this article, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking, and investigate the use of case adaptation for service composition. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilizes OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services.


Author(s):  
Fouad Henni ◽  
Baghdad Atmani

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.


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