Preprint Article Version 1 This version is not peer-reviewed

Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis

Version 1 : Received: 14 January 2018 / Approved: 15 January 2018 / Online: 15 January 2018 (11:29:56 CET)

How to cite: Wasim, M.U.; Ibrahim, A.A.Z.A.; Bouvry, P.; Limba, T. Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis. Preprints 2018, 2018010125 (doi: 10.20944/preprints201801.0125.v1). Wasim, M.U.; Ibrahim, A.A.Z.A.; Bouvry, P.; Limba, T. Cloud Service Providers Optimized Ranking Algorithm Based on Machine Learning and Multi-Criteria Decision Analysis. Preprints 2018, 2018010125 (doi: 10.20944/preprints201801.0125.v1).

Abstract

Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.

Subject Areas

multi-criteria decision analysis (MCDA); online broker; misspecification of criteria; structural uncertainty; unsupervised machine learning; factor analysis, quality of service (QoS)

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