FAVE: A fast and efficient network Flow AVailability Estimation method with bounded relative error. This paper focuses on helping network providers to carry out network capacity planning and sales projection by answering the question: For a given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as it flow availability, and present the underlineflow underlineavailability underlineestimation (FAVE) problem, which is a generalisation of network connectivity or maximum flow reliability estimations. Realistic networks are often large and dynamic, so flow availabilities cannot be evaluated analytically and simulation is often used. However, naive Monte Carlo (MC) or importance sampling (IS) techniques take an excessive amount of time. To quickly estimate flow availabilities, we utilize the correlations among link and flow failures to figure out the importance of roles played by different links in flow failures, and design three sequential importance sampling (SIS) methods which achieve bounded or even vanishing relative error with linear computational complexities. When applying to a realistic network, our method reduces the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods, respectively. Our method can also facilitate capacity planning by providing better flow availability guarantees, compared with traditional methods.

Measurementbased Online Available Bandwidth Estimation employing Reinforcement Learning. An accurate and fast estimation of the available bandwidth in a network with varying crosstraffic is a challenging task. The accepted probing tools, based on the fluidflow model of a bottleneck link with firstin, firstout multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation becomes more difficult if packet dispersions deviate from the assumptions of the fluidflow model in the presence of nonfluid bursty crosstraffic, multiple bottleneck links, and inaccurate timestamping. This motivates us to explore the use of machine learning tools for available bandwidth estimation. Hence, we consider reinforcement learning and implement the singlestate multiarmed bandit technique, which follows the epsilongreedy algorithm to find the available bandwidth. Our measurements and tests reveal that our proposed method identifies the available bandwidth with high precision. Furthermore, our method converges to the available bandwidth under a variety of notoriously difficult conditions, such as heavy traffic burstiness, different crosstraffic intensities, multiple bottleneck links, and in networks where the tight link and the bottleneck link are not same. Compared to the piecewise linear network a modelbased direct probing technique that employs a Kalman filter, our method shows more accurate estimates and faster convergence in certain network scenarios and does not require measurement noise statistics.