Efficient Cloud Server Deployment Under Demand Uncertainty

A main challenge faced by cloud providers is to ensure that they are ready to accommodate the growing demand for compute resources. Towards that goal, providers need to deploy cloud servers agilely for uncertain future demand under many practical business constraints while avoiding unnecessarily large operational costs. In this paper, we introduce the cloud server deployment problem. One important aspect of the problem is that the infrastructure preparation work has to be planned for before server deployments can take place. Furthermore, a combination of temporal constraints (e.g., projected dock dates) has to be considered together with a variety of physical constraints (e.g., hardware compatibility and daily deployment throughput at the data centers). While the problem shares similarities with other supply chain problems, its collection of characteristics requires new solutions that explicitly account for demand uncertainty. We formulate the underlying optimization problem as a two-stage stochastic program, and develop efficient and exact Benders-type algorithms that exploit the special structure of the second stage problem while accommodating different risk measures. We test our proposed algorithms with real production traces from Microsoft Azure, and demonstrate their effectiveness in cost reductions.