Handling Uncertainty and Diversity in Cloud Bandwidth Demands for Revenue Maximization

With the increasing demand for large bandwidth and diversity of bandwidth requests, to maximize the revenue, cloud providers nowadays try to offer different bandwidth request models that include guaranteed bandwidth reservation requests and on-demand flexible bandwidth requests. While guaranteed bandwidth reservation requests are beneficial for providers from the point of view of cash flow due to the upfront fee, it faces the problem of bandwidth under-utilization. On the other hand, on-demand flexible requests generate higher revenue, but they suffer from future demand uncertainty. Controlling the admission and trade-off between these kinds of requests while maximizing the revenue becomes a challenging problem for providers. In this paper, we present an optimal bandwidth allocation approach which supports the above bandwidth request models and maximizes the revenue for providers. We model the bandwidth allocation problem as a Markov Decision Process (MDP) which takes into account the utilization of guaranteed bandwidth reservation requests and the future demand uncertainty of on-demand flexible requests. We solve the MDP problem by using a dynamic programming algorithm. We demonstrate that the proposed model can be integrated into cloud data centers by leveraging on the new features of software defined networks to control the bandwidth for users. The numerical results show that the proposed model outperforms the baseline schemes and generates high revenue for providers.