A multiswarm for composite SaaS placement optimization based on PSO. (3rd July 2018)

• Record Type: Journal Article
• Authors: Chainbi, W.; Sassi, E.
• Other Names: Heilig Leonard guestEditor.; Lalla‐Ruiz Eduardo guestEditor.; Voß Stefan guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 48:Number 10(2018)
• Page Start: 1847
• Page End: 1864
• Publication Date: 2018-07-03
• Subjects: Cloud -- MPSO -- PSO -- SaaS placement; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2600
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Recently, the demand for software as a service (SaaS) has witnessed an increasing interest, which has raised many challenges for SaaS management. One of these challenges is to deliver a high performance composite SaaS for users while optimizing the resources used. In this paper, we focus on the problem of SaaS placement. This problem occurs in the deployment of SaaS components in Cloud. It deals with the way a composite SaaS should be placed in a Cloud by the Cloud's provider such that its performance is optimal based on its estimated execution time. Previous work, including metaheuristic methods and particle swarm optimization, focuses on resolving the problem in a static environment. Moreover, in a Cloud data center, the workloads of applications and resources capacities keep changing over time, and the environment is dynamic, so the solution found for the initial placement may need to be reconfigured to maintain the SaaS performance and to optimize the resource used. As multiswarm technique has attracted increasing attention during the last decade, in this paper, we propose a new placement solution based on such technique enhanced with a cooperative learning strategy to cope with the dynamic aspect of the Cloud.