网络应用，如网络状态跟踪、服务等级协议保障和网络故障定位等，依赖于完整准确的吞吐量测量数据.由于测量代价大，网络监控系统通常难以获得全网吞吐量测量数据.稀疏网络测量技术基于采样的方式降低测量代价，通过张量填充等算法挖掘数据内部的时空相关性，从部分网络测量数据恢复缺失数据.然而，现有研究仅考虑了单个性能指标，忽略了多个指标之间的关联信息，导致恢复精度受限且整体测量代价依然很大.本文提出了一个面向大规模网络测量的数据恢复算法——基于关联学习的张量填充（Association Learning based Tensor Completion，ALTC）.为了捕获网络性能指标之间的复杂关系，设计了一个关联学习模型，使用低测量开销的往返时延推测高测量开销的吞吐量，降低网络测量代价.在此基础上设计了一个张量填充模型，同时学习吞吐量测量数据内部的时空相关性和来自往返时延的外部辅助关联信息，最终以更高的恢复精度获取全网吞吐量数据.实验表明，在相同的吞吐量测量代价下，本文所提算法的恢复误差比目前主流方法的恢复误差降低了13%，达到了更好的恢复效果.

Network applications, such as network state tracking, service level agreement guarantee, and network fault location, rely on complete and accurate throughput measurement data. Due to the high measurement cost, it is hard to obtain network-wide throughput measurement data for network monitoring systems. Sparse network measurement techniques reduce the measurement cost based on sampling and recover missing data from partial network measurement data by exploiting spatio-temporal correlations within the data through algorithms such as tensor completion. However, existing studies only consider individual performance metrics and ignore the correlation information between multiple metrics, resulting in limited recovery accuracy and high overall measurement cost. This paper proposes a data recovery algorithm for large-scale network measurements—association learning based tensor completion(ALTC). To capture the complex correlations among network performance metrics, an association learning model is designed to reduce the network measurement cost by using the round-trip delay with low measurement overhead to infer the throughput with high measurement overhead. Based on this, a tensor completion model is designed to learn both the spatio-temporal correlation within the throughput measurement data and the external auxiliary correlation information from the round-trip delay, and finally obtain the network-wide throughput data with higher recovery accuracy. Experiments show that the recovery error of the proposed algorithm is 13% lower than that of the current mainstream methods at the same throughput measurement cost, achieving better recovery results.