By Topic

An Adaptive Model of Service Composition Based on Policy Driven and Multi-Agent Negotiation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jing-fan Tang ; College of Computer Science & Technology, Hangzhou Dianzi University, Hangzhou 310018, China. E-MAIL: ; Xiao-liang Xu

This paper presents an adaptive model of service composition to achieve the optimum profits in the emerging services market, which is based on policy driven and multi-agent negotiation. The negotiation rules will be defined in the policies to determine the conditions for composition and cooperation, such as the accepted range of QoS with corresponding price charge. An agent group will be established to achieve the policy driven negotiation process for service composition, which includes policy agent, evaluation agent and action agent. Policy agent will be responsible for acquiring and parsing negotiation rules from pre-defined policies. Action agent will be responsible for the interactions among the services to reach an agreement. During the negotiation process, evaluation agent will be responsible for doing the evaluation on the bid information received from action agent according to the negotiation rules. And, the action instructions will be generated and sent to action agent for the negotiation on the bids provided by services. Through such policy driven and multi-agent negotiation approach, it can address win-win in the composition and cooperation of the services

Published in:

2006 International Conference on Machine Learning and Cybernetics

Date of Conference:

13-16 Aug. 2006