In recent years, end-to-end network latency estimation has attracted much attention because of its significance for network performance evaluation. Given the widespread use of personal devices, latency estimation from partially observed samples becomes more complicated due to unstable communication conditions, while measuring the latencies between all nodes in a large-scale network is infeasible and costly. Hence, reducing the measurement cost becomes critical for the latency estimation of personal device network. In this paper, we propose an adaptive sampling scheme based on leverage scores to reduce the measurement cost while achieving high estimation accuracy. Furthermore, we provide theoretical analysis to characterize the performance bounds of the proposed scheme in terms of sampling complexity and estimation error. Finally, we demonstrate the efficiency of the proposed scheme by conducting extensive simulations on both synthetic and real datasets. The results show that the proposed scheme is able to not only improve the estimation accuracy of network latency but also reduce the sample budget compared to the state-of-the-art approaches.

中文翻译：

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具有个人设备杠杆采样的网络延迟估计：一种自适应张量完成方法

近年来，端到端网络等待时间估计由于其对网络性能评估的重要性而备受关注。鉴于个人设备的广泛使用，由于不稳定的通信条件，根据部分观察到的样本进行的延迟估计变得更加复杂，而大规模网络中所有节点之间的延迟测量却不可行且成本很高。因此，降低测量成本对于个人设备网络的等待时间估计变得至关重要。在本文中，我们提出了一种基于杠杆得分的自适应采样方案，以降低测量成本，同时实现较高的估计精度。此外，我们提供了理论分析，以根据采样复杂度和估计误差来表征所提出方案的性能界限。最后，我们通过对合成数据集和真实数据集进行广泛的仿真来证明所提出方案的效率。结果表明，与现有技术相比，该方案不仅可以提高网络等待时间的估计精度，而且可以减少样本预算。