Pricing for Revenue Maximization in Inter-DataCenter Networks.

As more applications and businesses move to the cloud, pricing for inter-datacenter links has become an important. problem. In this paper, we study revenue maximizing pricing from the perspective of a network provider in inter-datacenter networks. Designing a practical bandwidth pricing scheme requires us to jointly consider the requirements of envy-freeness and arbitrage-freeness, where envy -freeness guarantees the fairness of resource allocation and arbitrage -freeness induces users to truthfully reveal their data transfer requests. Considering the non -convexity of the revenue maximization problem and the lack of information about the users' utilities, we propose a framework for computationally efficient pricing to approximately maximize revenue in a range of environments. We first study the case of a single link accessed by many users, and design a (1 H-approximation-approximationpricing scheme with polynomial time complexity and information complexity. Rased on dynamic programming, we then extend the pricing scheme for the tollbooth network, preserving the (1+F) approximation ratio and the computational complexity. For the general network setting, we analyze the revenue generated by uniform pricing, which determines a single per unit price for all potential users. We show that when users have similar utilities, uniform pricing can achieve a good approximation ratio, which is independent of network topology and data transfer requests. The pricing framework can be extended to multiple time slots, enabling time -dependent pricing.