Multivariate Log-based Anomaly Detection for Distributed Database
arxiv(2024)
摘要
Distributed databases are fundamental infrastructures of today's large-scale
software systems such as cloud systems. Detecting anomalies in distributed
databases is essential for maintaining software availability. Existing
approaches, predominantly developed using Loghub-a comprehensive collection of
log datasets from various systems-lack datasets specifically tailored to
distributed databases, which exhibit unique anomalies. Additionally, there's a
notable absence of datasets encompassing multi-anomaly, multi-node logs.
Consequently, models built upon these datasets, primarily designed for
standalone systems, are inadequate for distributed databases, and the prevalent
method of deeming an entire cluster anomalous based on irregularities in a
single node leads to a high false-positive rate. This paper addresses the
unique anomalies and multivariate nature of logs in distributed databases. We
expose the first open-sourced, comprehensive dataset with multivariate logs
from distributed databases. Utilizing this dataset, we conduct an extensive
study to identify multiple database anomalies and to assess the effectiveness
of state-of-the-art anomaly detection using multivariate log data. Our findings
reveal that relying solely on logs from a single node is insufficient for
accurate anomaly detection on distributed database. Leveraging these insights,
we propose MultiLog, an innovative multivariate log-based anomaly detection
approach tailored for distributed databases. Our experiments, based on this
novel dataset, demonstrate MultiLog's superiority, outperforming existing
state-of-the-art methods by approximately 12
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