Title: Cloud manufacturing service composition based on business matching optimisation

Authors: Rim Drira; Hamdi Gabsi; Henda Hajjami Ben Ghezala

Addresses: RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2000, Tunisia ' RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2000, Tunisia ' RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2000, Tunisia

Abstract: Cloud manufacturing (CMfg) aims to create dedicated manufacturing solution with lower operating cost and faster time-to-market by combining available manufacturing resources and capabilities. In order to insure a dedicated manufacturing cloud matching the business' requirements, cloud service composition is considered a decisive process. Due to the large diversity of resources within similar functions and different quality of services (QoSs), the composition process can be a challenging task. In fact, this is known as composition plan selection (CPS) problem. The need for timely resolving this problem motivates the adoption of meta-heuristics algorithms like artificial bee colony (ABC) and genetic algorithm (GA). We propose in this paper, an improved ABC algorithm for the CPS problem which considers both business and QoS attributes for services composition. Experiments are done in order to tune precisely our algorithm and to compare it with similar state of the art algorithms.

Keywords: cloud service composition; meta-heuristic; quality of services; QoSs; artificial bee colony; ABC; genetic algorithm; parameters tuning; business matching.

DOI: 10.1504/IJWS.2022.122985

International Journal of Web Science, 2022 Vol.3 No.3, pp.204 - 235

Received: 30 Jul 2021
Accepted: 02 Sep 2021
Published online: 19 May 2022 *

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