FineMon: An Innovative Adaptive Network Telemetry Scheme for Fine-Grained, Multi-Metric Data Monitoring with Dynamic Frequency Adjustment and Enhanced Data Recovery
Summary: FineMon: adaptive network telemetry with Two-sided Frequency Adjustment (TFA) for fine-grained multi-metric monitoring; theoretical NMS frequency via metric-rank changes and real-time infra adjustment. ESTC robustly recovers noisy data in a tensor model, enabling accurate reconstruction with lower overhead; validated on three real datasets, improving accuracy and temporal-feature capture. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Haojie Ji (Hunan University)
- 2. Kun Xie (Hunan University)
- 3. Jigang Wen (Hunan University)
- 4. Qingyi Zhang (Huawei)
- 5. Gaogang Xie (Chinese Academy of Sciences; University of Chinese Academy of Sciences)
- 6. Wei Liang (Hunan University)
BibTeX Citation
@inproceedings{ji_sigmod24,
title = {{FineMon: An Innovative Adaptive Network Telemetry Scheme for Fine-Grained, Multi-Metric Data Monitoring with Dynamic Frequency Adjustment and Enhanced Data Recovery}},
author = {Ji, Haojie and Xie, Kun and Wen, Jigang and Zhang, Qingyi and Xie, Gaogang and Liang, Wei},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639267},
url = {https://dl.acm.org/doi/10.1145/3639267},
year = {2024}
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| 3,326 | Timon: A Timestamped Event Database for Efficient Telemetry Data Processing and Analytics | 2020 | SIGMOD | 7.5187553e-05 |
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