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Notably, some recent methods rely on supervised learning which necessitates a substantial amount of labeled data. However, labeling root cause instances is time-consuming and laborious, especially with multiple modalities of data including logs, traces, metrics, and so on. Moreover, some approaches favor deep learning for localization but lack interpretability and continuous improvement mechanisms.<\/jats:p>\n          <jats:p>\n            To address the above challenges, we propose\n            <jats:italic>DeepHunt<\/jats:italic>\n            , a novel root cause localization method based on multimodal data analysis. Firstly,\n            <jats:italic>DeepHunt<\/jats:italic>\n            introduces root cause score (RCS) by integrating reconstruction errors and failure propagation patterns (upstream\u2013downstream relationships), imparting interpretability to the localization of root causes. Then, it embraces graph autoencoder (GAE) to address the limitation imposed by scarce labeled data. It employs data augmentation to mitigate the adverse effects of insufficient historical training samples. We evaluate\n            <jats:italic>DeepHunt<\/jats:italic>\n            on two open source datasets, and it outperforms existing methods when facing a zero-label cold start.\n            <jats:italic>DeepHunt<\/jats:italic>\n            can be further improved by continuously fine-tuning through a feedback mechanism.\n          <\/jats:p>","DOI":"10.1145\/3695999","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:56:18Z","timestamp":1726235778000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Interpretable Failure Localization for Microservice Systems Based on Graph Autoencoder"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-7899","authenticated-orcid":false,"given":"Yongqian","family":"Sun","sequence":"first","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1268-2372","authenticated-orcid":false,"given":"Zihan","family":"Lin","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2513-5635","authenticated-orcid":false,"given":"Binpeng","family":"Shi","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0330-0028","authenticated-orcid":false,"given":"Shenglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9671-8667","authenticated-orcid":false,"given":"Shiyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3849-5478","authenticated-orcid":false,"given":"Pengxiang","family":"Jin","sequence":"additional","affiliation":[{"name":"Alibaba (Beijing) Software Services Co., Ltd., Beijing Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2114-5308","authenticated-orcid":false,"given":"Zhenyu","family":"Zhong","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4261-2467","authenticated-orcid":false,"given":"Lemeng","family":"Pan","sequence":"additional","affiliation":[{"name":"AI Application Research Center, Huawei Technologies Co., Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7238-8254","authenticated-orcid":false,"given":"Yicheng","family":"Guo","sequence":"additional","affiliation":[{"name":"AI Application Research Center, Huawei Technologies Co., Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-838X","authenticated-orcid":false,"given":"Dan","family":"Pei","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing Shi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467174"},{"issue":"1","key":"e_1_3_2_3_2","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An Jinwon","year":"2015","unstructured":"Jinwon An and Sungzoon Cho. 2015. 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