A survey on intelligent management of alerts and incidents in IT services

Yu, Qingyang; Zhao, Nengwen; Li, Mingjie; Li, Zeyan; Wang, Honglin; Zhang, Wenchi; Sui, Kaixin; Pei, Dan

doi:10.1016/j.jnca.2024.103842

Journal of Network and Computer Applications

2024

DOI: 10.1016/j.jnca.2024.103842

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A survey on intelligent management of alerts and incidents in IT services

Qingyang Yu,

Nengwen Zhao,

Mingjie Li

et al.

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Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Zha,

Shan,

et al. 2024

Electronics

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Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated alerts. Therefore, effective alert aggregation is crucial in isolating root causes and accelerating failure resolution. Existing approaches typically rely on either semantic similarity or statistical methods, both of which have significant limitations, such as ignoring causal relationships or struggling to handle infrequent alerts. To overcome these drawbacks, we propose a novel two-phase alert aggregation approach. We employ temporal–spatial clustering to group alerts based on their temporal proximity and spatial attributes. In the second phase, we utilize large language models to trace the cascading effects of service failures and aggregate alerts that share the same root cause. Experimental evaluations on datasets from real-world cloud platforms demonstrate the effectiveness of our method, achieving superior performance compared to traditional aggregation techniques.

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Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Zha,

Shan,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

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A survey on intelligent management of alerts and incidents in IT services

Cited by 1 publication

References 84 publications

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

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