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<p>This paper proposes a novel method for network latency estimation. Network latency estimation is a crucial indicator for evaluating network performance, yet accurate estimation of large-scale network latency requires substantial computation time. Therefore, this paper introduces a method capable of enhancing the speed of network latency estimation. The paper represents the data structure of network nodes as matrices and introduces a time dimension to form a tensor model, thereby transforming the entire network latency estimation problem into a tensor completion problem. The main contributions of this paper include: optimizing leveraged sampling for tensors to improve sampling speed, and on this basis, introducing the Qatar Riyal (QR) decomposition of tensors into the Alternating Direction Method of Multipliers (ADMM) framework to accelerate tensor completion; these two components are combined to form a new model called LNLS-TQR. Numerical experimental results demonstrate that this model significantly improves computation speed while maintaining high accuracy.</p>

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Tensor completion via leverage sampling and tensor QR decomposition for network latency estimation

  • 期刊论文

  • 发表日期：

    2025年4月24日

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  • Jun Lei,
  • Jiqian Zhao,
  • Jingqi Wang,
  • An-Bao Xu

摘要

This paper proposes a novel method for network latency estimation. Network latency estimation is a crucial indicator for evaluating network performance, yet accurate estimation of large-scale network latency requires substantial computation time. Therefore, this paper introduces a method capable of enhancing the speed of network latency estimation. The paper represents the data structure of network nodes as matrices and introduces a time dimension to form a tensor model, thereby transforming the entire network latency estimation problem into a tensor completion problem. The main contributions of this paper include: optimizing leveraged sampling for tensors to improve sampling speed, and on this basis, introducing the Qatar Riyal (QR) decomposition of tensors into the Alternating Direction Method of Multipliers (ADMM) framework to accelerate tensor completion; these two components are combined to form a new model called LNLS-TQR. Numerical experimental results demonstrate that this model significantly improves computation speed while maintaining high accuracy.