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.

中文翻译：

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FAVE：具有有限相对误差的快速高效的网络流可用性估计方法

容量规划和销售计划是网络运营商的基本任务。这项工作旨在通过回答以下问题来帮助网络提供商进行网络容量规划和销售预测：给定拓扑和容量，网络是否可以高概率满足当前的流量需求？我们将这种可能性称为“ 流量可用性 ”，并通过概括基于经典网络连接性和基于最大流量的可靠性估计来提出流量可用性估计（FAVE）问题。为了快速估计流量可用性，我们利用链接和流量故障之间的相关性来找出不同链接在流量故障中扮演的角色的重要性（即，流量需求无法得到满足）。我们设计了三种顺序重要性抽样（SIS）估计方法，它们是：（1）准确高效 ：它们具有线性计算复杂性，可实现有限甚至消失的相对误差。因此，它们可以在更少的仿真时间内提供更准确的估计。（2）强大且可扩展：即使只有部分SEED设置信息可用，或者当FAVE问题扩展到多流情况时，它们仍保持这样的估计效率。当应用于现实的骨干网络时，与MC和基线IS方法相比，我们的方法可以将流量可用性估算成本降低900和130倍。与传统方法相比，通过提供更好的流程可用性保证，还可以促进产能计划和销售预测。