A new strategy based on approximate dynamic programming to maximize the net revenue of IaaS cloud providers with limited resources

Abstract With rapid development of cloud computing, more conventional Internet Data Centers get involved into this business model. Providers of small and medium sized data centers have relatively limited computing resources compared to global IaaS providers such as Amazon. The demand for computing at these service providers may be above the available capacity especially during peak shopping periods (e.g. mother’s day or singles’ day). However, it is undesirable to blindly expand capacity, which will not only incur the purchase cost of new machines but also the maintenance costs associated with resource over-provisioning. For these service providers, the most important thing is how to effectively manage resources during peak demand periods to maximize their net revenue. In this context, we focus on the joint admission control and virtual machine placement problem, and propose a dynamic optimization model considering that customers dynamically create and terminate virtual machines in cloud computing environment. In terms of problem complexity, the developed dynamic optimization model typically suffers from “curse of dimensionality” and is computationally intractable. So we resort to approximate dynamic programming framework and propose a new strategy to yield a tractable model for real-time decision-making. Extensive simulations are conducted to compare our proposed strategy with other strategies including existing methods of virtual machine placement. The simulation results demonstrate that our customized strategy achieves substantial revenue improvements and the improvement is more significant as resource provisioning is tighter. In addition, it can be scalable to solve large cases up to 1000 physical machines and yield a real-time decision in a reasonable time.