Metadata-Based Retrieval for Resolution Recommendation in AIOps
For a cloud service provider, the goal is to proactively identify
signals that can help reduce outages and/or reduce the mean-time-to-detect and mean-time-to-resolve. After an incident is reported, the Site Reliability Engineers diagnose the fault and search for a resolution by formulating a textual query to find similar historical incidents - this approach is called text-based retrieval. However, it has been observed that the formulated queries are inadequate and short. An alternate approach, presented in this paper, integrates information spread across heterogeneous and siloed datasets, as a ready-to-use knowledge base for metadata-based resolution retrieval. Additionally, it exploits historical problem context for building metadata prediction models which are used at run-time for automatically formulating queries from log anomalies detected by the Log Anomaly Detection module. The query, thus formed, is run against the metadata-based index, unlike the text-based index in text retrieval, resulting in superior performance, in terms of relevancy of the resolution documents retrieved. Through experiments on web application server applications deployed on the cloud, we show the efficacy of metadata-based retrieval, which not only returns targeted results as compared to text-based retrieval but also the relevant resolution document appear amongst the top 3 positions for 60% of the queries.
Tue 15 NovDisplayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change
14:00 - 15:30 | Debugging/localizationResearch Papers / Industry Paper / Demonstrations / Ideas, Visions and Reflections at SRC LT 51 Chair(s): Mauro Pezze USI Lugano; Schaffhausen Institute of Technology | ||
14:00 15mTalk | Metadata-Based Retrieval for Resolution Recommendation in AIOps Industry Paper DOI | ||
14:15 15mTalk | PaReco: Patched Clones and Missed Patches among the Divergent Variants of a Software Family Research Papers Poedjadevie Kadjel Ramkisoen University of Antwerp; Flanders Make, John Businge University of Antwerp; Flanders Make; University of Nevada at Las Vegas, Brent van Bladel University of Antwerp; Flanders Make, Alexandre Decan University of Mons; F.R.S.-FNRS, Serge Demeyer University of Antwerp; Flanders Make, Coen De Roover Vrije Universiteit Brussel, Foutse Khomh Polytechnique Montréal DOI | ||
14:30 15mTalk | Fault Localization to Detect Co-change Fixing Locations Research Papers Yi Li New Jersey Institute of Technology, Shaohua Wang New Jersey Institute of Technology, Tien N. Nguyen University of Texas at Dallas DOI | ||
14:45 15mTalk | Reflections on Software Failure Analysis Ideas, Visions and Reflections Paschal Amusuo Purdue University, Aishwarya Sharma Purdue University, Siddharth R. Rao Purdue University, Abbey Vincent Purdue University, James C. Davis Purdue University DOI | ||
15:00 7mTalk | eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps Demonstrations Kowndinya Boyalakuntla Indian Institute of Technology Tirupati, Marimuthu Chinnakali National Institute of Technology Karnataka, Sridhar Chimalakonda IIT Tirupati, K. Chandrasekaran National Institute of Technology Karnataka | ||
15:08 7mTalk | SFLKit: A Workbench for Statistical Fault Localization Demonstrations Marius Smytzek CISPA Helmholtz Center for Information Security, Andreas Zeller CISPA Helmholtz Center for Information Security Pre-print |