Title: Load balancing, cost and response time minimisation issues in agent-based multi cloud service composition
Authors: Ouassila Hioual; Zizette Boufaïda; Sofiane Mounine Hemam
Addresses: Department of Mathematics and Computer Science, Abbes Laghrour University, Khenchela, 40004, Algeria; LIRE Laboratory, Contantine, 25000, Algeria ' Department of Software Technologies and Information Systems, Constantine 2 University-Abdelhamid Mehri, Constantine, 25000, Algeria; LIRE Laboratory, Contantine, 25000, Algeria ' Department of Mathematics and Computer Science, Abbes Laghrour University, Khenchela, 40004, Algeria; ICOSI Laboratory, Khenchela, 40004, Algeria
Abstract: In multi cloud environments, we need to find services from multiple clouds, if a single cloud cannot give us all the required component services. In such a situation, however, it is challenging to find an appropriate composition sequence by taking into consideration load balancing between the different clouds and in the same time by minimising the composite service cost and response time. In this paper, an agent-based cloud service composition architecture is presented. It includes two main agents CSCA and CMA which use MCDA methods to support cloud services selection. These agents use load balancing mechanisms and the CNP in order to evolve and adapt cloud service composition. We have performed a comprehensive analytical and experimental study to evaluate the effectiveness of our approach. The experimental results, based on CloudSim simulator, show that the proposed architecture can effectively achieve good performance (load balancing), improve the response time and minimise cost.
Keywords: cloud services; composition; load balancing; multi cloud computing; cost minimisation; multi agent system; multi-criteria decision analysis; contract net protocol; CNP; cloud computing; TOPSIS; analytic hierarchy process; AHP.
International Journal of Internet Protocol Technology, 2017 Vol.10 No.2, pp.73 - 88
Received: 18 Jan 2016
Accepted: 15 Sep 2016
Published online: 27 Jun 2017 *