Congestion-Constrained Virtual Link Embedding with Uncertain Demands

Network virtualization enables multiple virtual net-works to co-exist on the same physical network. Each virtual network requires specific amounts of physical network resources such as node processing and link bandwidth. The problem of mapping virtual resource requirements to physical resources is extensively studied in the literature under the assumption that resource demands of virtual networks are known deterministically. In real deployments though, resource demands include significant uncertainty and fluctuate over time. This paper considers the problem of mapping virtual links to physical network paths subject to a constraint on each virtual link congestion probability under the assumption that bandwidth demands of virtual links are uncertain. A general uncertainty model is considered, where bandwidth demands are described by random variables for which only the mean and variance (or a range) are known. We formulate the problem as a nonlinear optimization problem, which is shown to be non-convex. Consequently, we develop an approximate formulation that results in a second-order cone program (SOCP) that can be solved efficiently even for large networks. We then provide simulation as well as Mininet experimental results to show the utility and efficiency of our exact and approximate models in various network scenarios. We apply our models to commonly studied USA and EON networks as well as randomly generated large networks. Our results show that both models are able to satisfy the link congestion constraint, and that the approximate model is very close to the exact model.