Game Theory-Based Nonlinear Bandwidth Pricing For Congestion Control In Cloud Networks

In the cloud, the network links are shared among tenants, which makes them easy to get fully congested (overloaded). Overloaded links degrade the performance of tenants' applications, and impose additional costs to the cloud provider. In this paper, we propose a nonlinear bandwidth pricing policy for congestion control in the cloud network. In order to maximize social welfare (i. e., maximize the total satisfaction of the tenants while minimizing the congestion over the link), the cloud provider uses the nonlinear pricing policy that increases the unit price with increment of bandwidth usage. Each tenant competes for bandwidth allocation to maximize its utility (i. e., both maximize its own individual satisfaction and minimize its bandwidth payment cost). We design a game between tenants and the cloud provider, and show that there exists a unique optimal bandwidth schedule (Nash equilibrium) that jointly maximizes the social welfare and the utility of each tenant at the same time. In order to find the optimal schedule, we use an asynchronous-based best response strategy, in which each tenant updates its optimal bandwidth allocation based on the updated bandwidth payment function from the cloud provider. We prove that the updated bandwidth allocations converge to the optimal bandwidth schedule. In our simulation study and real implementation, we verify the performance of our proposed pricing mechanism under different scenarios.