Multi-tenant SaaS deployment optimisation algorithm for cloud computing environment. (2018)

• Record Type: Journal Article
• Authors: Ming, Cao; Bingjie, Yu; Xiantong, Liu
• Is Part Of: International journal of internet protocol technology. Volume 11:Number 3(2018)
• Page Start: 152
• Page End: 158
• Publication Date: 2018
• Subjects: software as a service -- SaaS -- multi-tenant -- SPP -- ant colony algorithm -- ACO -- MapReduce; File Transfer Protocol (Computer network protocol) -- Periodicals; Multicasting (Computer networks) -- Periodicals; 004.678
• Languages: English
• ISSNs: 1743-8209

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.