Multi-tenant SaaS deployment optimisation algorithm for cloud computing environment
by Cao Ming; Yu Bingjie; Liu Xiantong
International Journal of Internet Protocol Technology (IJIPT), Vol. 11, No. 3, 2018

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.

Online publication date: Wed, 05-Sep-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Internet Protocol Technology (IJIPT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com