Authors:
Kadda Beghdad Bey
1
;
Hassina Nacer
2
;
Mohamed El Yazid Boudaren
1
and
Farid Benhammadi
1
Affiliations:
1
Ecole Militaire Polytechnique, Algeria
;
2
University of Science and Technology, Algeria
Keyword(s):
Cloud Computing, Resource Allocation, Software as a Service (SaaS), Services Discovery, Web Service, Multi-agents Systems, Clustering Methods, Matching.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Cloud Computing
;
Collaboration and e-Services
;
Data Engineering
;
Distributed and Mobile Software Systems
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Mobile Software and Services
;
Modeling of Distributed Systems
;
Multi-Agent Systems
;
Ontologies and the Semantic Web
;
Semantic Web Technologies
;
Services Science
;
Software Agents and Internet Computing
;
Software Engineering
;
Software Engineering Methods and Techniques
;
Symbolic Systems
;
Telecommunications
;
Web Services
;
Wireless Information Networks and Systems
Abstract:
Cloud computing is an emerging new computing paradigm in which both software and hardware resources are provided through the internet as a service to users. Software as a Service (SaaS) is one among the important services offered through the cloud that receive substantial attention from both providers and users. Discovery of services is however, a difficult process given the sharp increase of services number offered by different providers. A Multi-agent system (MAS) is a distributed computing paradigm-based on multiple interacting agents- aiming to solve complex problems through a decentralized approach. In this paper, we present a novel approach for SaaS service discovery based on Multi-agents systems in cloud computing environments. More precisely, the purpose of our approach is to satisfy the user’s needs in terms of both result accuracy rate and processing time of the request. To establish the interest of the proposed solution, experiments are conducted on a simulated dataset.