Self-Regulated Multi-criteria Decision Analysis: An Autonomous Brokerage-Based Approach for Service Provider Ranking in the Cloud

The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias if the customer has insufficient domain knowledge. He/she may exclude rel-evant or include irrelevant criterion termed as ’misspecification of crite-rion’. This causes structural uncertainty within the MCDA leading to se-lection of suboptimal service provider by online broker. To cater such issue, we propose a self-regulated MCDA, which uses notion of factor analysis from the field of unsupervised machine learning. Two QoS based datasets were used for evaluation of proposed model. The prior 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 later 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 results show better performance of proposed model as compared to its counterparts in the field. The beneficiary of the research would be enterprises that view insufficient domain knowledge as a limiting factor for acquisition of Cloud services.