{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T04:50:11Z","timestamp":1768971011756,"version":"3.49.0"},"reference-count":82,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62032016"],"award-info":[{"award-number":["62032016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Science and Technology Program","award":["CJGJZD20230724091659002"],"award-info":[{"award-number":["CJGJZD20230724091659002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Microservice-based systems often suffer from reliability issues due to their intricate interactions and expanding scale. With the rapid growth of observability techniques, various methods have been proposed to achieve failure diagnosis, including root cause localization and failure type identification, by leveraging diverse monitoring data such as logs, metrics, or traces. However, traditional failure diagnosis methods that use single-modal data can hardly cover all failure scenarios due to the restricted information. Several failure diagnosis methods have been recently proposed to integrate multimodal data based on deep learning. These methods, however, tend to combine modalities indiscriminately and treat them equally in failure diagnosis, ignoring the relationship between specific modalities and different diagnostic tasks. This oversight hinders the effective utilization of the unique advantages offered by each modality. To address the limitation, we propose\n                    <jats:italic toggle=\"yes\">TVDiag<\/jats:italic>\n                    , a multimodal failure diagnosis framework for locating culprit microservice instances and identifying their failure types (e.g., Net-packets Corruption) in microservice-based systems.\n                    <jats:italic toggle=\"yes\">TVDiag<\/jats:italic>\n                    employs task-oriented learning to enhance the potential advantages of each modality and establishes cross-modal associations based on contrastive learning to extract view-invariant failure information. Furthermore, we develop a graph-level data augmentation strategy that randomly inactivates the observability of some normal microservice instances to mitigate the shortage of training data. Experimental results on four datasets show that\n                    <jats:italic toggle=\"yes\">TVDiag<\/jats:italic>\n                    outperforms the state-of-the-art methods in multimodal failure diagnosis by at least 20.16% and 3.08% in terms of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(HR@1\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and F1-score, respectively.\n                  <\/jats:p>","DOI":"10.1145\/3734868","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T11:37:51Z","timestamp":1746704271000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework for Microservice-based Systems with Multimodal Data"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7925-3788","authenticated-orcid":false,"given":"Shuaiyu","family":"Xie","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1559-9314","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China and Zhongguancun Laboratory, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0871-9041","authenticated-orcid":false,"given":"Hanbin","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5966-0325","authenticated-orcid":false,"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2642-5109","authenticated-orcid":false,"given":"Yuqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central China Normal University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8662-5690","authenticated-orcid":false,"given":"Neng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2165-2636","authenticated-orcid":false,"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China and Zhongguancun Laboratory, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"AIOps. 2024. Retrieved from https:\/\/competition.aiops-challenge.com"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/ICSE.2019.00031","volume-title":"2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE)","author":"Amar Anunay","year":"2019","unstructured":"Anunay Amar and Peter C. Rigby. 2019. Mining historical test logs to predict bugs and localize faults in the test logs. In 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE, 140\u2013151."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377813.3381353"},{"key":"e_1_3_2_5_2","unstructured":"Stephen J. Bigelow. 2022. What is Observability? A Beginner\u2019s Guide. Retrieved from https:\/\/www.techtarget.com\/searchitoperations\/definition\/observability\/"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski Piotr","year":"2017","unstructured":"Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5 (2017), 135\u2013146.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"e_1_3_2_7_2","unstructured":"chaosmesh. 2025. Retrieved from https:\/\/chaos-mesh.org"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-022-01622-7"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2016.2607739"},{"key":"e_1_3_2_10_2","first-page":"1597","volume-title":"International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_11_2","unstructured":"Yinfang Chen Manish Shetty Gagan Somashekar Minghua Ma Yogesh Simmhan Jonathan Mace Chetan Bansal Rujia Wang and Saravan Rajmohan. 2025. AIOpsLab: A holistic framework to evaluate AI agents for enabling autonomous clouds. arXiv:2501.06706. Retrieved from https:\/\/arxiv.org\/abs\/2501.06706"},{"issue":"11","key":"e_1_3_2_12_2","first-page":"2213","article-title":"Spell: Online streaming parsing of large unstructured system logs","volume":"31","author":"Du Min","year":"2018","unstructured":"Min Du and Feifei Li. 2018. Spell: Online streaming parsing of large unstructured system logs. IEEE Transactions on Knowledge and Data Engineering 31, 11 (2018), 2213\u20132227.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"e_1_3_2_14_2","first-page":"27503","article-title":"Efficiently identifying task groupings for multi-task learning","volume":"34","author":"Fifty Chris","year":"2021","unstructured":"Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn. 2021. Efficiently identifying task groupings for multi-task learning. Advances in Neural Information Processing Systems 34 (2021), 27503\u201327516.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_15_2","unstructured":"C. N. C. Foundation. 2024. OpenTelemetry. Retrieved from https:\/\/opentelemetry.io\/docs\/concepts\/sampling\/"},{"key":"e_1_3_2_16_2","first-page":"103","volume-title":"2014 IEEE International Conference on Cluster Computing (CLUSTER)","author":"Fu Xiaoyu","year":"2014","unstructured":"Xiaoyu Fu, Rui Ren, Sally A. McKee, Jianfeng Zhan, and Ninghui Sun. 2014. Digging deeper into cluster system logs for failure prediction and root cause diagnosis. In 2014 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 103\u2013112."},{"key":"e_1_3_2_17_2","unstructured":"GAIA. 2024. Retrieved from https:\/\/github.com\/CloudWise-OpenSource\/GAIA-DataSet"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304013"},{"key":"e_1_3_2_19_2","unstructured":"Google. 2024. Retrieved from https:\/\/github.com\/GoogleCloudPlatform\/microservices-demo"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2023.3241299"},{"key":"e_1_3_2_21_2","first-page":"1387","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Guo Xiaofeng","year":"2020","unstructured":"Xiaofeng Guo, Xin Peng, Hanzhang Wang, Wanxue Li, Huai Jiang, Dan Ding, Tao Xie, and Liangfei Su. 2020. Graph-based trace analysis for microservice architecture understanding and problem diagnosis. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 1387\u20131397."},{"key":"e_1_3_2_22_2","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017), 1024\u20131034.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510110"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2017.13"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236083"},{"key":"e_1_3_2_26_2","first-page":"1","volume-title":"37th IEEE\/ACM International Conference on Automated Software Engineering","author":"He Zilong","year":"2022","unstructured":"Zilong He, Pengfei Chen, Yu Luo, Qiuyu Yan, Hongyang Chen, Guangba Yu, and Fangyuan Li. 2022. Graph based incident extraction and diagnosis in large-scale online systems. 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IEEE, 1750\u20131762."},{"key":"e_1_3_2_30_2","first-page":"1724","volume-title":"2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE)","author":"Lee Cheryl","year":"2023","unstructured":"Cheryl Lee, Tianyi Yang, Zhuangbin Chen, Yuxin Su, Yongqiang Yang, and Michael R. Lyu. 2023. Heterogeneous anomaly detection for software systems via semi-supervised cross-modal attention. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 1724\u20131736."},{"issue":"1","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10664-021-10063-9","article-title":"Enjoy your observability: An industrial survey of microservice tracing and analysis","volume":"27","author":"Li Bowen","year":"2022","unstructured":"Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, and Xuanzhe Liu. 2022. Enjoy your observability: An industrial survey of microservice tracing and analysis. 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In Proceedings of the 20th International Conference on Service-Oriented Computing (ICSOC \u201922). Springer, 219\u2013236."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521340"},{"issue":"8","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1142\/S0218194022500395","article-title":"Root cause analysis of anomalies based on graph convolutional neural network","volume":"32","author":"Li Zhongliang","year":"2022","unstructured":"Zhongliang Li, Yaofeng Tu, and Zongmin Ma. 2022. Root cause analysis of anomalies based on graph convolutional neural network. International Journal of Software Engineering and Knowledge Engineering 32, 8 (2022), 1155\u20131177.","journal-title":"International Journal of Software Engineering and Knowledge Engineering"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549092"},{"key":"e_1_3_2_37_2","unstructured":"Lukas Liebel and Marco K\u00f6rner. 2018. Auxiliary tasks in multi-task learning. arXiv:1805.06334. Retrieved from https:\/\/arxiv.org\/abs\/1805.06334"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03596-9_1"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2889160.2889232"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103680"},{"key":"e_1_3_2_41_2","first-page":"338","volume-title":"2021 IEEE\/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","author":"Liu Dewei","year":"2021","unstructured":"Dewei Liu, Chuan He, Xin Peng, Fan Lin, Chenxi Zhang, Shengfang Gong, Ziang Li, Jiayu Ou, and Zheshun Wu. 2021. Microhecl: High-efficient root cause localization in large-scale microservice systems. In 2021 IEEE\/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 338\u2013347."},{"key":"e_1_3_2_42_2","first-page":"413","volume-title":"2008 8th IEEE International Conference on Data Mining","author":"Fei","year":"2008","unstructured":"Fei, Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 8th IEEE International Conference on Data Mining. IEEE, 413\u2013422."},{"key":"e_1_3_2_43_2","unstructured":"Jay Livens. 2023. What is Observability? Not Just Logs Metrics and Traces. Retrieved from https:\/\/www.dynatrace.com\/news\/blog\/what-is-observability-2\/"},{"key":"e_1_3_2_44_2","volume-title":"Advances in Neural Information Processing Systems","author":"Lundberg Scott M.","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/8a20a8621978632d76c43dfd28b67767-Paper.pdf"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2020.2993251"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380111"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWQoS49365.2020.9213058"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpds.2013.21"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/978-3-030-44769-4_13","volume-title":"Proceedings of the 8th IFIP WG 2.14 European Conference on Service-Oriented and Cloud Computing (ESOCC \u201920)","author":"Nedelkoski Sasho","year":"2020","unstructured":"Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, and Odej Kao. 2020. Multi-source distributed system data for ai-powered analytics. In Proceedings of the 8th IFIP WG 2.14 European Conference on Service-Oriented and Cloud Computing (ESOCC \u201920). Springer, 161\u2013176."},{"key":"e_1_3_2_50_2","unstructured":"OpenTracing. 2024. OpenTracing. Retrieved from https:\/\/opentracing.io\/specification"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464805"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3558951"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.1994.10476030"},{"key":"e_1_3_2_54_2","first-page":"805","volume-title":"Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201920)","author":"Qiu Haoran","year":"2020","unstructured":"Haoran Qiu, Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer. 2020. FIRM: An intelligent fine-grained resource management framework for slo-oriented microservices. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201920), 805\u2013825."},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/CLOUD55607.2022.00072","volume-title":"2022 IEEE 15th International Conference on Cloud Computing (CLOUD)","author":"Rios Jesus","year":"2022","unstructured":"Jesus Rios, Saurabh Jha, and Laura Shwartz. 2022. Localizing and explaining faults in microservices using distributed tracing. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD). IEEE, 489\u2013499."},{"key":"e_1_3_2_56_2","first-page":"1","volume-title":"Proceedings of the 14th ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)","author":"Rosenberg Carl Martin","year":"2020","unstructured":"Carl Martin Rosenberg and Leon Moonen. 2020. Spectrum-based log diagnosis. In Proceedings of the 14th ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 1\u201312."},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313653"},{"key":"e_1_3_2_58_2","unstructured":"Sockshop. 2025. Retrieved from https:\/\/github.com\/microservices-demo\/microservices-demo"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2023.3293890"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3691620.3695495"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3691620.3695489"},{"key":"e_1_3_2_62_2","unstructured":"TVDiag. 2024. Retrieved from https:\/\/github.com\/WHU-AISE\/TVDiag"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00252"},{"issue":"10","key":"e_1_3_2_64_2","first-page":"e2433","article-title":"The operation and maintenance governance of microservices architecture systems: A systematic literature review","volume":"35","author":"Wang Lu","year":"2022","unstructured":"Lu Wang, Yu Xuan Jiang, Zhan Wang, Qi En Huo, Jie Dai, Sheng Long Xie, Rui Li, Ming Tao Feng, Yue Shen Xu, and Zhi Ping Jiang. 2022. The operation and maintenance governance of microservices architecture systems: A systematic literature review. Journal of Software: Evolution and Process 35, 10 (2022), e2433.","journal-title":"Journal of Software: Evolution and Process"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/ICWS49710.2020.00026","volume-title":"2020 IEEE International Conference on Web Services (ICWS)","author":"Wang Lingzhi","year":"2020","unstructured":"Lingzhi Wang, Nengwen Zhao, Junjie Chen, Pinnong Li, Wenchi Zhang, and Kaixin Sui. 2020. Root-cause metric location for microservice systems via log anomaly detection. In 2020 IEEE International Conference on Web Services (ICWS). 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IEEE, 1\u20136."},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3376202"},{"key":"e_1_3_2_72_2","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. 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