Robust Virtual Network Allocation under Uncertainty of Traffic Demands and Renewable Energy Power

In this paper, we consider a physical network powered by renewable energy resources and a virtual network (VN) of a client service, which is composed of a client node, a virtual machine (VM) node, and a virtual link. Then, a robust VN allocation problem is formulated for multiple client services: given the location of client nodes, find the allocation of VM nodes and virtual links to maintain robustness for the uncertainty of traffic demands and renewable energy power; i.e., their prediction error. Specifically, we propose two robust allocation models: robust VN allocation model and two-stage robust VN allocation model, which are formulated on the basis of robust optimization and two-stage robust optimization, respectively. To show the effectiveness of two robust proposed models, we conduct numerical experiments under various prediction error patterns of traffic demands and renewable energy power. When prediction errors are large, the two proposed models acquire better average and worst-case performance than a deterministic model that does not handle prediction errors. In addition, we observe some patterns where the two-stage robust VN allocation model acquires better average performance than the robust VN allocation model instead of deteriorating the worst-case performance.