IEICE TRANSACTIONS on Communications
A KPI Anomaly Detection Method Based on Fast Clustering
Summary :
In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.11 pp.1309-1317
- Publication Date
- 2022/11/01
- Publicized
- 2022/05/27
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021TMP0002
- Type of Manuscript
- Special Section PAPER (Special Section on Towards Management for Future Communications and Services in Conjunction with Main Topics of APNOMS2021)
- Category
Authors
Yun WU
Northeast Electric Power University
Yu SHI
Northeast Electric Power University
Jieming YANG
Northeast Electric Power University
Lishan BAO
Industry Group Co. Ltd.
Chunzhe LI
State Grid Jilin Electric Power Co. Ltd.
Keyword
Latest Issue
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Yun WU, Yu SHI, Jieming YANG, Lishan BAO, Chunzhe LI, "A KPI Anomaly Detection Method Based on Fast Clustering" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 11, pp. 1309-1317, November 2022, doi: 10.1587/transcom.2021TMP0002.
Abstract: In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021TMP0002/_p
Copy
@ARTICLE{e105-b_11_1309,
author={Yun WU, Yu SHI, Jieming YANG, Lishan BAO, Chunzhe LI, },
journal={IEICE TRANSACTIONS on Communications},
title={A KPI Anomaly Detection Method Based on Fast Clustering},
year={2022},
volume={E105-B},
number={11},
pages={1309-1317},
abstract={In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.},
keywords={},
doi={10.1587/transcom.2021TMP0002},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - A KPI Anomaly Detection Method Based on Fast Clustering
T2 - IEICE TRANSACTIONS on Communications
SP - 1309
EP - 1317
AU - Yun WU
AU - Yu SHI
AU - Jieming YANG
AU - Lishan BAO
AU - Chunzhe LI
PY - 2022
DO - 10.1587/transcom.2021TMP0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2022
AB - In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
ER -
English
