|Zhenzhe Zheng||Shanghai Jiao Tong University, P.R. China|
|R Srikant||University of Illinois at Urbana-Champaign, USA|
|Guihai Chen||Shanghai Jiao Tong University, P.R. China|
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 +)-approximation pricing scheme with polynomial time complexity and information complexity. Based on dynamic programming, we then extend the pricing scheme for the tollbooth network, preserving the (1+) 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.