Automatic and Generic Periodicity Adaptation for KPI Anomaly Detection

Zhao, Ning; Zhu, Jiang; Wang, Yao; Ma, Minghua; Zhang, Wenchi; Liu, Dapeng; Zhang, Ming; Pei, Dan

doi:10.1109/tnsm.2019.2919327

Cited by 35 publications

(8 citation statements)

References 28 publications

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“…To carry out this process, the following two values are required: “T” : Period of the main seasonal pattern of the KPI. It can be calculated with the system proposed in [ 19 ] that was introduced in the previous section. “S” : Number of previous KPI samples that the system will use to detect new anomalous behaviors.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…“T” : Period of the main seasonal pattern of the KPI. It can be calculated with the system proposed in [ 19 ] that was introduced in the previous section.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Thus, it is critical that the KPI patterns are characterized and updated over time. In this sense, the authors of [ 19 ] proposed a system which detects the different seasonal patterns of a KPI in real time. This system uses a density-based spatial clustering of applications with noise (DBSCAN) algorithm.…”

Section: Online Anomaly Detectionmentioning

confidence: 99%

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Online Anomaly Detection System for Mobile Networks

Burgueño

de-la-Bandera

Mendoza

et al. 2020

Sensors

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The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.

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Section: Proposed Methodologymentioning

confidence: 99%

“…“T” : Period of the main seasonal pattern of the KPI. It can be calculated with the system proposed in [ 19 ] that was introduced in the previous section.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Online Anomaly Detection System for Mobile Networks

Burgueño

de-la-Bandera

Mendoza

et al. 2020

Sensors

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“…Normal and abnormal are already marked in these datasets. e KPI dataset is from the AIOps Challenge held by Tsinghua University in 2018 [29]. Many Internet companies monitor the data generated by various performance indicators in order to ensure the stability of web services, such as CPU usage and server health, and other performance indicators.…”

Section: E Comparisons Of Using Different Datasetsmentioning

confidence: 99%

Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

Rong

OuYang

Sun

2022

Mobile Information Systems

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With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.

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“…Nowadays, online service systems, such as online shopping, Ebank, and search engines, have become an indispensable part in our daily life. Although tremendous efforts have been devoted to software service maintenance (e.g., collecting various monitoring data for a service system such as metrics [44,46,54], logs [19,31,51], traces [55], and alerts [29]), due to their large scale and complexity, incidents (i.e., unplanned interruption/outage to a service [2, 16,25]) are still inevitable, which could lead to system unavailability and huge economic loss [32]. For example, according to a recent survey [1], the average cost per hour of server downtime is between $301,000 and $400,000.…”

Section: Introductionmentioning

confidence: 99%