Recent decades have observed an exponential growth in network traffic, thanks to the increased popularity of real-time applications, such as live video chat and gaming. The resulting growth in the network infrastructure has made it difficult for the service providers to abide by the service level agreements, especially with regards to the quality-of-service guarantees. Predicting network latencies from noisy and missing measurements has therefore emerged as an important problem, and a plethora of solutions have been proposed for the same. Existing network latency predictions rely either on Euclidean embedding or matrix completion methods. This work considers the estimation and prediction of network latencies from a sequence of noisy and incomplete latency matrices collected over time. An adaptive matrix completion algorithm is proposed that can handle streaming data at low computational complexity. The performance of the proposed algorithm is characterized both in theory and using a real dataset, demonstrating its viability as a network monitoring tool.

中文翻译：

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基于噪声测量的自适应网络延迟预测

由于实时视频聊天和游戏等实时应用程序的日益普及，近几十年来网络流量呈指数级增长。网络基础设施的增长导致服务提供商难以遵守服务级别协议，尤其是在服务质量保证方面。因此，从噪声和丢失的测量结果预测网络等待时间已成为一个重要问题，并且为此提出了许多解决方案。现有的网络等待时间预测依赖于欧几里得嵌入或矩阵完成方法。这项工作考虑了随着时间推移收集的一系列嘈杂和不完整的等待时间矩阵对网络等待时间的估计和预测。提出了一种自适应矩阵完成算法，该算法可以以低计算复杂度处理流数据。所提出算法的性能在理论上和使用实际数据集均得到了表征，证明了其作为网络监控工具的可行性。