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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

ISBN: 978-989-758-247-9

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.

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Paper citation in several formats:
Bey , K.; Nacer, H.; Boudaren, M. and Benhammadi, F. (2017). A Novel Clustering-based Approach for SaaS Services Discovery in Cloud Environment.In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-247-9, pages 546-553. DOI: 10.5220/0006328205460553

@conference{iceis17,
author={Kadda Beghdad Bey . and Hassina Nacer. and Mohamed El Yazid Boudaren. and Farid Benhammadi.},
title={A Novel Clustering-based Approach for SaaS Services Discovery in Cloud Environment},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2017},
pages={546-553},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006328205460553},
isbn={978-989-758-247-9},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - A Novel Clustering-based Approach for SaaS Services Discovery in Cloud Environment
SN - 978-989-758-247-9
AU - Bey , K.
AU - Nacer, H.
AU - Boudaren, M.
AU - Benhammadi, F.
PY - 2017
SP - 546
EP - 553
DO - 10.5220/0006328205460553

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