LADDERS: Log Based Anomaly Detection and Diagnosis for Enterprise Systems
Enterprise software can fail due to not only malfunction of application servers, but also due to performance degradation or non-availability of other servers or middle layers. Consequently, valuable time and resources are wasted in trying to identify the root cause of software failures. To address t...
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| Published in | Annals of data science Vol. 11; no. 4; pp. 1165 - 1183 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.08.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2198-5804 2198-5812 |
| DOI | 10.1007/s40745-023-00471-7 |
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| Abstract | Enterprise software can fail due to not only malfunction of application servers, but also due to performance degradation or non-availability of other servers or middle layers. Consequently, valuable time and resources are wasted in trying to identify the root cause of software failures. To address this, we have developed a framework called LADDERS. In LADDERS, anomalous incidents are detected from log events generated by various systems and KPIs (Key Performance Indicators) through an ensemble of supervised and unsupervised models. Without transaction identifiers, it is not possible to relate various events from different systems. LADDERS implements Recursive Parallel Causal Discovery (RPCD) to establish causal relationships among log events. The framework builds coresets using BICO to manage high volumes of log data during training and inferencing. An anomaly can cause a number of anomalies throughout the systems. LADDERS makes use of RPCD again to discover causal relationships among these anomalous events. Probable root causes are revealed from the causal graph and anomaly rating of events using a k-shortest path algorithm. We evaluated LADDERS using live logs from an enterprise system. The results demonstrate its effectiveness and efficiency for anomaly detection. |
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| AbstractList | Enterprise software can fail due to not only malfunction of application servers, but also due to performance degradation or non-availability of other servers or middle layers. Consequently, valuable time and resources are wasted in trying to identify the root cause of software failures. To address this, we have developed a framework called LADDERS. In LADDERS, anomalous incidents are detected from log events generated by various systems and KPIs (Key Performance Indicators) through an ensemble of supervised and unsupervised models. Without transaction identifiers, it is not possible to relate various events from different systems. LADDERS implements Recursive Parallel Causal Discovery (RPCD) to establish causal relationships among log events. The framework builds coresets using BICO to manage high volumes of log data during training and inferencing. An anomaly can cause a number of anomalies throughout the systems. LADDERS makes use of RPCD again to discover causal relationships among these anomalous events. Probable root causes are revealed from the causal graph and anomaly rating of events using a k-shortest path algorithm. We evaluated LADDERS using live logs from an enterprise system. The results demonstrate its effectiveness and efficiency for anomaly detection. |
| Author | Mondal, Sakib A. Menon, Arun Rao, Sagar Rv, Prashanth |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| References_xml | – reference: Roy S, König A.C, Dvorkin I, Kumar M (2015) Perfaugur: robust diagnostics for performance anomalies in cloud services. In: 2015 IEEE 31st International conference on data engineering, IEEE, pp. 1167–1178 – reference: Ding R, Fu Q, Lou J.G, Lin Q, Zhang D, Xie T (2014) Mining historical issue repositories to heal large-scale online service systems. 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In: Proceedings of the 36th international conference on software engineering, pp. 468–479 – reference: Yen T.-F, Oprea A, Onarlioglu K, Leetham T, Robertson W, Juels A, Kirda E (2013) Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks. In: Proceedings of the 29th annual computer security applications conference, pp. 199–208 – reference: Xu W, Huang L, Fox A, Patterson D, Jordan MI (2009) Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd symposium on operating systems principles. pp. 117–132 – reference: Fu Q, Lou J.-G, Wang Y, Li J (2009) Execution anomaly detection in distributed systems through unstructured log analysis. In: 2009 Ninth IEEE International conference on data mining, IEEE, pp. 149–158 – reference: Steck H, Tresp V (1999) Bayesian belief networks for data mining. 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In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp. 499–508 – reference: RadanlievPDe RoureDWaltonRVan KleekMSantosOMaddoxLWhat country, university, or research institute, performed the best on covid-19 during the first wave of the pandemic? bibliometric analysis of scientific literature-analysing a ‘snapshot in time’ of the first wave of covid-19Ann Data Sci2022951049106710.1007/s40745-022-00406-8 – reference: TienJMInternet of things, real-time decision making, and artificial intelligenceAnn Data Sci2017414917810.1007/s40745-017-0112-5 – reference: CinqueMCotroneoDPecchiaAEvent logs for the analysis of software failures: a rule-based approachIEEE Trans Softw Eng201239680682110.1109/TSE.2012.67 – reference: Lin Q, Zhang H, Lou J.-G, Zhang Y, Chen X (2016) Log clustering based problem identification for online service systems. 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