FAVE: A Fast and Efficient Network Flow AVailability Estimation Method With Bounded Relative Error

Abstract

Capacity planning and sales projection are essential tasks for network operators. This work aims to help network providers to carry out network capacity planning and sales projection by answering: Given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as the ``flow availability'', and present the flow availability estimation (FAVE) problem with generalizing the classical network connectivity based and maximum flow based reliability estimations. To quickly estimate flow availabilities, we utilize correlations among link and flow failures to figure out the importance of roles played by different links in flow failures (i.e., flow demands could not be satisfied). And we design three sequential importance sampling (SIS) estimation methods, which are: (1) Accurate and efficient: They achieve a bounded or even vanishing relative error with linear computational complexities. Hence they can provide more accurate estimations in less simulation time. (2) Robust and scalable: They maintain such estimation efficiencies even if only a partial SEED set information is available, or when the FAVE problem is extended to the multiple flows case. When applying to a realistic backbone network, our method can reduce the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods; and also facilitate capacity planning and sales projection by providing better flow availability guarantees, compared with traditional methods.