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- W4320029507 abstract "Network latency is an important metric for network performance evaluation. However, device faults inevitably occur during the data collection process, resulting in abnormal data or even missing data. It is desirable to accurately estimate the network latencies and detect the abnormal data. Existing approaches can not provide reliable performance due to the limitation in exploiting the spatial-temporal correlations of latency data. In this paper, we propose a novel Robust Spatial-Temporal Graph-Tensor Recovery (RSTGTR) algorithm which simultaneously recovers the missing data and detects the anomalies in network latencies. Firstly, we model the network latency data as a novel graph-tensor for exploring the topological structure of networks. Secondly, we develop spatial-temporal constraints and propose a graph-tensor recovery algorithm (RSTGTR). Finally, we conduct extensive experiments to evaluate the performance of the proposed algorithm by using a real-world latency dataset. Experimental results show the proposed algorithm outperforms existing methods in terms of latency estimation and anomaly detection." @default.
- W4320029507 created "2023-02-12" @default.
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- W4320029507 date "2022-12-04" @default.
- W4320029507 modified "2023-09-26" @default.
- W4320029507 title "Robust Spatial-Temporal Graph-Tensor Recovery for Network Latency Estimation" @default.
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- W4320029507 doi "https://doi.org/10.1109/globecom48099.2022.10001361" @default.
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