C-DRM: Coalesced P-TOPSIS Entropy Technique addressing Uncertainty in Cloud Service Selection





Cloud Computing, Service Computing, Entropy, Uncertainty, Decision Making


Cloud Computing is diversified with its services exponentially and lured large number of consumers towards the technology indefinitely. It has become a highly challenging problem to satiate the user requirements. Most of the existing system ingest large search space or provide inappropriate service; hence, there is a need for the reliable and space competent service selection/ranking in the cloud environment. The proposed work introduces a novel pruning method and Dual Ranking Method (DRM) to rank the services from n services in terms of space conserving and providing reliable service quenching the user requirements as well. Dual Ranking Method (DRM) is proposed focusing on the uncertainty of user preferences along with their priorities; converting it to weights with the use of Jensen-Shannon (JS) Entropy Function. The ranking of service is employed through Priority-Technique for Order of Preference by Similarity to Ideal Solution (P-TOPSIS) and space complexity is reduced by novel Utility Pruning method. The performance of the proposed work  Clustering – Dual Ranking Method (C-DRM) is estimated in terms of accuracy, Closeness Index (CI) and space complexity have been validated through case study where results outperforms the existing approaches

Author Biography

Pabitha Parameshwaran, Madras Institute of Technology Campus, Anna University

Department of Computer Technology, Madras Institute of Technology Campus, Anna University, Chennai, Tamilnadu, India