Title: Multi-tenant SaaS deployment optimisation algorithm for cloud computing environment

Authors: Cao Ming; Yu Bingjie; Liu Xiantong

Addresses: State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050000, China ' State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050000, China ' State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050000, China

Abstract: This article adopts MapReduce and multi-targeted ant colony algorithm (ACO) distribution in parallel to solve large-scaled service dynamic selection in SaaS and puts forward a service dynamic selection algorithm based on these technologies. The algorithm integrates cloud calculation technologies such as loading strategy, ACO, MapReduce, and HDFS, which deploys the service to servers as little as possible, to further save the energy target. Meanwhile, it also takes into account the smallest price target deployment and server loading balancing target, which transforms the global optimisation service dynamic selection into a multi-targeted service combination optimisation problem with QoS restriction. The simulation experiments verify and prove the feasibility, effectiveness and convergence of the improved algorithm.

Keywords: software as a service; SaaS; multi-tenant; SPP; ant colony algorithm; ACO; MapReduce.

DOI: 10.1504/IJIPT.2018.094531

International Journal of Internet Protocol Technology, 2018 Vol.11 No.3, pp.152 - 158

Received: 19 Dec 2017
Accepted: 15 Mar 2018
Published online: 05 Sep 2018 *

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