A Ranking Based Model for Selecting Optimum Cloud Geographical Region
Neeraj1, Major Singh Goraya2, Damanpreet Singh3

1Neeraj, Department of CSE, Sant Longowal Institute of Engineering and Technology,Longowal Sangrur, India.
2Major Singh, Department of CSE, Sant Longowal Institute of Engineering and Technology, Longowal Sangrur, India.
3Damanpreet Singh, Department of CSE, Sant Longowal Institute of Engineering and Technology, Longowal Sangrur, India.

Manuscript received on 03 July 2019 | Revised Manuscript received on 08 July 2019 | Manuscript published on 30 August 2019 | PP: 793-797 | Volume-8 Issue-10, August 2019 | Retrieval Number: J89080881019/19©BEIESP | DOI: 10.35940/ijitee.J8908.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cloud computing has become a dominant service computing model where the services such as software, platform, infrastructure are provided to the cloud consumers on demand basis using pay as you go model. Every cloud consumer requires high performance service in minimal cost. The performance of service in cloudis measured by the parameters availability, agility, cost, and security etc. The service performance and cost are very much dependent on the cloud geographical region (CGR) where these are deployed. The services offered by the cloud service provider are installed on multiple data centers located at different CGR. In cloud environment, the selection of a service installed on the optimum CGR within the limited time overhead is a challenging and interesting problem. In this paper, the multi criteria decision making method, PROMETHEE II and objective weighting method Shannon’s Entropy,based ranking model is proposed for solving the optimum CGR selection problem. The CGR dataset of Amazon Web Service is used for the numerical analysis. The sensitivity analysis is performed for validating the stability of the proposed model and getting the most sensitive parameter. The applicability and usefulness of the service selection process is validated through the experimental results on synthetic dataset. Results show that the service selection process is achieved withlimited time overhead and hence suitable for online selection process in cloud.
Keywords: Cloud computing, PROMETHEE, Performance, Service selection.
Scope of the Article: Cloud computing