CloudRank: A statistical modelling framework for characterizing user behaviour towards targeted cloud management

A rank clustering system, CloudRank, is proposed that takes into account cloud user preference data to characterize cloud user behaviour and also identify (an initially unknown set of) groups of users with similar behaviour in an unsupervised manner. The user groups are determined based on fitting mixture models on the cloud user preference observations. A preference can be anything that a system designer would like to include to characterize high-level user requirements such as demands on performance, cost, security, availability, etc. CloudRank can be useful for: (i) cloud providers to target their service offerings according to the user groups (i.e. customer segments) through appropriate customization of services pertaining to the user groups typical requirements; (ii) recommendation systems or a marketplace (that enables inter-operability among different providers) to determine which offerings best suit certain user groups; and (iii) prediction of any new users behaviour based on their preference information. Results on realistic feedbacks from internal cloud service providers show an average of 80% accuracy of the proposed unsupervised technique. When compared with a supervised technique, i.e. when the number of user groups are known beforehand, the error is within 15%, thus making it a promising technique for realistic deployments, particularly when there is no prior knowledge regarding the clusters.