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Towards Pay-As-You-Consume Cloud Computing

Cloud computing enables users to perform their computation tasks in the public virtualized cloud using a pay-as-you-go style. Current pay-as-you-go pricing schemes typically charge on the incurred virtual machine hours. Our case studies demonstrate significant variations in the user costs, indicating significant unfairness among different users from the micro-economic perspective. Further studies reveal the reason for such variations is interference among concurrent virtual machines. The amount of interference cost depends on various factors, including workload characteristics, the number of concurrent VMs, and scheduling in the cloud. In this paper, we adopt the concept of pricing fairness from micro economics, and quantitatively analyze the impact of interference on the pricing fairness. To solve the unfairness caused by interference, we propose a pay-as-you-consume pricing scheme, which charges users according to their effective resource consumption excluding interference. The key idea behind the pay-as-you-consume pricing scheme is a machine learning based prediction model of the relative cost of interference. Our preliminary results with Xen demonstrate the accuracy of the prediction model, and the fairness of the pay-as-you-consume pricing scheme.