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Welcome to AIOps@NKU lab!

Our lab focuses on artificial intelligence for IT operations (AIOps) and the resilience of complex information infrastructures. We study the reliable operation of large-scale cloud platforms, data-center networks, cloud-native systems, domestic operating systems, and AI infrastructure. Our research seeks to characterize anomalies in heterogeneous operational data, align information across modalities, and identify dynamic causal relationships, thereby enabling system- and component-level anomaly detection, failure early warning, root-cause localization, and intelligent recovery. We develop techniques based on generative modeling, language models, multimodal learning, causal inference, knowledge graphs, reinforcement learning, and large language model (LLM)-based agents. These techniques analyze operational evidence such as metrics, logs, traces, dependency and topology data, historical incidents, and operating procedures.

We are also exploring AIOps agents and human-agent collaboration. Current topics include automated on-call response, multi-agent incident management, explainable zero-shot root-cause localization, LLM-based operating-system fault diagnosis, and automated orchestration of remediation workflows. Our goal is to provide efficient, explainable, and verifiable support throughout fault discovery, diagnosis, and recovery, while keeping domain experts involved in critical decisions.

Our group has published more than 100 papers in AIOps, including over 50 papers in CCF-recommended A-class venues, and has undertaken more than 40 industry-academia projects. Our technologies have been applied or validated in real-world scenarios at organizations including State Grid, China Mobile, Huawei, Tencent, ByteDance, Alibaba Cloud, the Big Data Center of the Ministry of Emergency Management, and Industrial and Commercial Bank of China.

We welcome motivated undergraduate students interested in AIOps, dependable systems, and AI agents. Students may participate in projects using open-source benchmarks and, subject to project authorization and data-access requirements, real-world operational data. Through these projects, students can gain hands-on experience in research, system development, and application-oriented evaluation. This experience can also help prepare them for graduate study and for internships or careers at leading research institutions and technology companies.

Our lab is organized by Dr. Shenglin Zhang ,  Dr. Yongqian sun and Dr. Wenwei Gu. We have published a series of papers, which can be found here. In addition, here are the courses Dr. Shenglin Zhang, Dr. Yongqian Sun and Dr. Wenwei Gu taught.

News

  1. 7/5/2026:We get six papers accepted by ASE 2026 (CCF A)
  2. 6/5/2026: Our paper, “Graph of States: Solving Abductive Tasks with Large Language Models“, id accepted by International Conference on Machine Learning (ICML) (CCF A)
  3. 21/3/2026: We get two papers accepted by FSE 2026 (CCF A)
  4. 14/3/2026: Our paper, “LLM-Enhanced Failure Localization in Microservices: Integrating Multi-Modal Data and Expert Interpretation”,is accepted by IEEE Transactions on Service Computing (TSC) (CCF A)
  5. 1/12/2025: We get three papers accepted by ICSE 2026 (CCF A)
  6. 12/9/2025: Our paper, “Bridging Edge and Cloud: A Knowledge-Enhanced Framework for Efficient Time Series Anomaly Detection”,is accepted by IEEE Transactions on Service Computing (TSC) (CCF A)
  7. 12/9/2025: We get two papers accepted by ASE 2025 (CCF A)
  8. 5/8/2025:Our paper,”Accurate and Interpretable Log-Based Fault Diagnosis using Large Language Models”, is accepted by IEEE Transactions on Service Computing (TSC) (CCF A)
  9. 19/7/2025: We get four papers accepted by ISSRE 2025 (CCF B)
  10. 1/7/2025: Our paper, “AetherLog: Log-based Root Cause Analysis by Integrating Large Language Models with Knowledge Graphs“, is accepted by International Symposium on Software Reliability Engineering (ISSRE) (CCF B)
  11. 27/6/2025: Our paper, “Effective Node-Level Anomaly Detection in HPC Systems via Coarse-Grained Clustering and Fine-Grained Model Sharing”, is accepted by international Conference for High Performance Computing, Networking, Storage and Analysis (CCF A)
  12. 23/6/2025: Our paper,”LogEval: A Comprehensive Benchmark Suite for LLMs in Log Analysis”, is accepted by Empirical Software Engineering (CCF B)
  13. 17/5/2025: Our paper, “FlowXpert: Expertizing Troubleshooting Workflow Orchestration with Knowledge Base and Multi-Agent Coevolution” , is accepted by ACM SIGKDD Conference on Knowledge Discovery and Data Mining (CCF-A)
  14. 26/3/2025: Our paper, “Bridging the Gap: LLM-Powered Transfer Learning for Log Anomaly Detection in New Software Systems”, is accepted by International Conference on Data Engineering (CCF A)
  15. 25/3/2025: We get two papers accepted by FSE 2025 (CCF A)
  16. 30/10/2024: We received the only two Best Paper Awards at ISSRE ’24! One was for Best Research Paper and the other for Best Industry Paper. To the best of our knowledge, this is the first time that both awards have been given to the same team!
  17. 16/10/2024: Our paper, “Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Software Systems “, is accepted by ACM Transactions on Software Engineering and Methodology (CCF A)
  18. 16/10/2024: Our paper, “No More Data Silos: Unified Microservice Failure Diagnosis with Temporal Knowledge Graph”, is accepted by IEEE Transactions on Services Computing (CCF A)
  19. 08/09/2024: We get three papers accepted by ISSRE 2024 industry track (CCF B)
  20. 08/07/2024: We get one paper accepted and two papers conditionally accepted by ASE 2024 (CCF A)
  21. 08/05/2024: Professor Shenglin Zhang serves as the reviewer of KDD 2025 ADS Track
  22. 07/30/2024: Our paper, “LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices”, is accepted by ISSRE 2024 (CCF B)
  23. 05/17/2024: Our paper, “Microservice Root Cause Analysis With Limited Observability Through Intervention Recognition in the Latent Space”, is accepted by KDD (CCF A)
  24. 05/05/2024: Professor Shenglin Zhang serves as the Artifact Evaluation Chair of ISSRE 2024. Welcome to submit your work to ISSRE 2024!
  25. 04/27/2024: Our paper, “Diagnosing Performance Issues for Large-Scale Microservice Systems with Heterogeneous Graph”, is accepted by TSC (CCF A)
  26. 04/19/2024: We get two papers, “Fault Diagnosis for Test Alarms in Microservices Through Multi-source Data” and “Illuminating the Gray Zone: Non-Intrusive Gray Failure Localization in Server Operating Systems”, are accepted by ESEC/FSE 2024 (CCF A)
  27. 01/23/2024: Our paper, “Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web Systems”, is accepted by WWW 2024 (CCF A)
  28. 10/12/2023: Our paper, “AutoKAD: Empowering KPI Anomaly Detection with Label-Free Deployment”, won the Best Research Paper Award in ISSRE 2023!
  29. 07/31/2023: Our paper, “Assess and Summarize: Improve Outage Understanding with Large Language Models”, is accepted by ESEC/FSE 2023 (CCF A)
  30. 07/30/2023: Three papers are accepted by ISSRE 2023 (CCF B).
  31. 06/29/2023: Our paper, “LogKG: Log Failure Diagnosis through Knowledge Graph,” is also accepted by IEEE TSC (CCF A).
  32. 06/14/2023: Our paper, “Robust Failure Diagnosis of Microservice System through Multimodal Data,” is accepted by IEEE TSC (CCF A).
  33. 05/17/2023: Our paper, “Robust Multimodal Failure Detection for Microservice Systems”, is accepted by ACM SIGKDD 2023 (CCF A).
  34. 05/16/2023: Prof. Shenglin Zhang will be the TPC member of IEEE ISSRE 2023 (CCF B). Welcome to submit your work to IEEE ISSRE 2023!
  35. 05/09/2023: Our paper, “Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Web Services,” is accepted by ICWS 2023 (CCF B).
  36. 04/23/2023: Our paper, “Efficient and Robust KPI Outlier Detection for Large-Scale Datacenters”, is accepted by IEEE Transactions on Computers (CCF A).
  37. 02/27/2023: Prof. Shenglin Zhang will be the TPC member of IEEE ICNP 2023 (CCF B). Welcome to submit your work to IEEE ICNP 2023!
  38. 01/23/2023: Our paper, “CMDiagnostor: An Ambiguity-Aware Root Cause Localization Approach Based on Call Metric Data,” is accepted by WWW 23 (CCF A).
  39. 05/05/2022: Our paper, “Efficient KPI Anomaly Detection Through Transfer Learning for Large-Scale Web Services,” is accepted by IEEE JSAC (CCF A, Impact Factor: 9.144). Congratulations!
  40. 03/27/2022: Our paper, “Online Malicious Domain Name Detection with Partial Labels for Large-Scale Dependable Systems,” is accepted by The Journal of Systems & Software (CCF B, Impact Factor: 2.829). Congratulations!
  41. 02/28/2022: Dr. Shenglin Zhang will serve as the TPC member of IEEE ISSRE 2022 (CCF B). Welcome to submit your work to ISSRE 2022!
  42. 02/26/2022: Our paper, “Robust Anomaly Clue Localization of Multi-dimensional Derived Measure for Online Video Services,” is accepted by IEEE Transactions on Services Computing (CCF A, Impact Factor: 8.216). Congratulations!
  43. 02/20/2022: Dr. Shenglin Zhang will serve as the TPC member of IEEE/ACM IWQoS 2022 (CCF B). Welcome to submit your work to IWQoS 2022!
  44. 02/04/2022: Dr. Shenglin Zhang will serve as the TPC member of IEEE ICNP 2022 (CCF B). Welcome to submit your work to ICNP 2022!
  45. 01/14/2022: Our paper “Robust System Instance Clustering for Large-Scale Web Services” is accepted by WWW 22 (CCF A).
  46. Dr. Shenglin Zhang will serve as the TPC member of WWW 2022 (CCF A). Welcome to submit your work to WWW 2022!
  47. Our paper “Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems” is accepted by USENIX ATC (CCF A). Congratulations!
  48. Our paper, “Detecting Outlier Machine Instances through Gaussian Mixture Variational Autoencoder with One Dimensional CNN”, is accepted by IEEE Transactions on Computers (CCF A). Congratulations!
  49. Dr. Shenglin Zhang will serve as the TPC member of ISSRE 2021. Welcome to submit your work to ISSRE 2021!
  50. Our paper “LogClass: Anomalous Log Identification andClassification with Partial Labels” is accepted by IEEE TNSM.
  51. Our papers “Cross-System Log Anomaly Detection for Software Systems” and “Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks” are accepted by IEEE ISSRE 2020.
  52. Our paper, “Localizing Failure Root Causes in a Microservice through Causality Inference”, is accepted by IEEE/ACM IWQoS 2020.
  53. Our paper, “Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases”, is accepted by VLDB 2020.
  54. Our paper, “Efficient and Robust Syslog Parsing for Network Devices in Datacenter Networks”, is accepted by IEEE Access (JCR Zone 2). Congratulations!
  55. Our work is reported by Nature Research in the article entitled “Finding order in a swirl of information”, which introduces College of Software, Nankai University.
  56. Our paper, ” LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs”, is accepted by IJCAI 2019 (CCF A). Congratulations to Ms. Yuqing Liu, who is in her junior year at College of Software, Nankai University now.
  57. Our paper, “Causal Analysis of the Unsatisfying Experience in Realtime Mobile Multiplayer Games in the Wild”, is accepted by IEEE ICME 2019 (CCF B). Congratulations!
  58. Our paper, “Robust and Rapid Adaption for Concept Drift in Software System Anomaly Detection”, of which Dr. Shenglin Zhang is the corresponding author, win the “Best Research Paper Award” at IEEE ISSRE conference (CCF B). Congratulations!

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