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Changhua Pei - IEEE Xplore Author Profile
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Changhua Pei

Publications
9
Citations
6
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (60)
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Changhua Pei

Affiliation

Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences

Publication Topics

Biography

Changhua Pei received the BS and PhD degrees in computer science and technology from Tsinghua University in 2012 and 2017, respectively. He is currently an associate researcher with the Computer Network Information Center (CNIC) within the Chinese Academy of Sciences (CAS). His research interests encompass time-series anomaly detection, AIOps (AI for IT Operations), AI for networking, and networking.(Based on document published on 30 October 2023).
Publications
9
Citations
6
Publications by Year
20232024
Co-Authors:
Show All Co-Authors (60)
Author's Published Works

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Self-Evolutionary Group-wise Log Parsing Based on Large Language Model

Changhua Pei;Zihan Liu;Jianhui Li;Erhan Zhang;Le Zhang;Haiming Zhang;Wei Chen;Dan Pei;Gaogang Xie

2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2024 | Conference Paper |
Log parsing involves extracting appropriate templates from semi-structured logs, providing foundational information for downstream log analysis tasks such as anomaly detection and log comprehension. Initially, the task of log parsing was approached by domain experts who manually designed heuristic rules to extract templates. However, the effectiveness of these manual rules deteriorates when certai...Show More

Self-Evolutionary Group-wise Log Parsing Based on Large Language Model

Changhua Pei;Zihan Liu;Jianhui Li;Erhan Zhang;Le Zhang;Haiming Zhang;Wei Chen;Dan Pei;Gaogang Xie

Year: 2024 | Conference Paper |

SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces

Zhenhe Yao;Haowei Ye;Changhua Pei;Guang Cheng;Guangpei Wang;Zhiwei Liu;Hongwei Chen;Hang Cui;Zeyan Li;Jianhui Li;Gaogang Xie;Dan Pei

2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2024 | Conference Paper |
Microservice architecture has become a predominant paradigm in the software industry. This architecture necessitates robust end-to-end testing to ensure seamless integration of all components before deployment. Rapidly pinpointing issues when test cases fail is crucial for enhancing software development efficiency. However, in testing environments, the available trace is often sparse, and the syst...Show More

SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces

Zhenhe Yao;Haowei Ye;Changhua Pei;Guang Cheng;Guangpei Wang;Zhiwei Liu;Hongwei Chen;Hang Cui;Zeyan Li;Jianhui Li;Gaogang Xie;Dan Pei

Year: 2024 | Conference Paper |

TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models

Haotian Si;Jianhui Li;Changhua Pei;Hang Cui;Jingwen Yang;Yongqian Sun;Shenglin Zhang;Jingjing Li;Haiming Zhang;Jing Han;Dan Pei;Gaogang Xie

2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no effective way to verify whether they can meet the requirements for real-world deployment. Firstly, current algorithms typically train a specific model for each time series. Maintaining such many models is impractical in a...Show More

TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models

Haotian Si;Jianhui Li;Changhua Pei;Hang Cui;Jingwen Yang;Yongqian Sun;Shenglin Zhang;Jingjing Li;Haiming Zhang;Jing Han;Dan Pei;Gaogang Xie

Year: 2024 | Conference Paper |

Large Language Models Can Provide Accurate and Interpretable Incident Triage

Zexin Wang;Jianhui Li;Minghua Ma;Ze Li;Yu Kang;Chaoyun Zhang;Chetan Bansal;Murali Chintalapati;Saravan Rajmohan;Qingwei Lin;Dongmei Zhang;Changhua Pei;Gaogang Xie

2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2024 | Conference Paper |
Large-scale cloud services frequently experience incidents that can have a significant impact on their stability. Incident triage is a critical process that assigns incidents to dedicated teams for resolution. However, traditional rule-based methods, commonly employed in various systems, have limitations due to a finite set of rules that necessitate continuous updates, leading to suboptimal perfor...Show More

Large Language Models Can Provide Accurate and Interpretable Incident Triage

Zexin Wang;Jianhui Li;Minghua Ma;Ze Li;Yu Kang;Chaoyun Zhang;Chetan Bansal;Murali Chintalapati;Saravan Rajmohan;Qingwei Lin;Dongmei Zhang;Changhua Pei;Gaogang Xie

Year: 2024 | Conference Paper |

Causality Enhanced Graph Representation Learning for Alert-Based Root Cause Analysis

Zhaoyang Yu;Qianyu Ouyang;Changhua Pei;Xin Wang;Wenxiao Chen;Liangfei Su;Huai Jiang;Xuanrun Wang;Jianhui Li;Dan Pei

2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Year: 2024 | Conference Paper |
Accurate and efficient root cause identification in online service systems is critical for service stability and user experience. When a system failure occurs, numerous alerts are generated, but existing methods fail to effectively integrate all these multi-modal data to pinpoint the root causes. Moreover, most existing approaches are inefficient for large-scale online services due to their high r...Show More

Causality Enhanced Graph Representation Learning for Alert-Based Root Cause Analysis

Zhaoyang Yu;Qianyu Ouyang;Changhua Pei;Xin Wang;Wenxiao Chen;Liangfei Su;Huai Jiang;Xuanrun Wang;Jianhui Li;Dan Pei

Year: 2024 | Conference Paper |

ActiveDNS: Is There Room for DNS Optimization Beyond CDNs?

Yue Wang;Changhua Pei;Zexin Wang;Yingqiang Wang;Guo Chen;Yuchao Zhang;Yi Li;Jingjing Li;Jianhui Li;Gaogang Xie

2024 IEEE 49th Conference on Local Computer Networks (LCN)
Year: 2024 | Conference Paper |
Domain Name System (DNS) converts domain names into IP addresses. The specific IP it returns to a client has significant implications for optimizing user experiences on web services. The establishment of Content Distribution Networks (CDNs) facilitates the spread of content across diverse cache servers, thereby enabling quick responses to user requests. But not all distributed Internet services ha...Show More

ActiveDNS: Is There Room for DNS Optimization Beyond CDNs?

Yue Wang;Changhua Pei;Zexin Wang;Yingqiang Wang;Guo Chen;Yuchao Zhang;Yi Li;Jingjing Li;Jianhui Li;Gaogang Xie

Year: 2024 | Conference Paper |

DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting

Zulong Diao;Xin Wang;Dafang Zhang;Gaogang Xie;Jianguo Chen;Changhua Pei;Xuying Meng;Kun Xie;Guangxing Zhang

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 6 | Journal Article |
Cited by: Papers (3)
Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time. Exploiting such data to forecast future traffic conditions has the potential to enhance the decision-making capabilities of intelligent transportation systems, which attracts widespread attention from both industries and academia. Among them, network-wide prediction based on graph convolutional neural netwo...Show More

DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting

Zulong Diao;Xin Wang;Dafang Zhang;Gaogang Xie;Jianguo Chen;Changhua Pei;Xuying Meng;Kun Xie;Guangxing Zhang

Year: 2024 | Volume: 23, Issue: 6 | Journal Article |

AutoKAD: Empowering KPI Anomaly Detection with Label-Free Deployment

Zhaoyang Yu;Changhua Pei;Shenglin Zhang;Xidao Wen;Jianhui Li;Gaogang Xie;Dan Pei

2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Monitoring Key Performance Indicators (KPIs) and detecting anomalies in online service systems is critical. However, choosing the right KPI anomaly detection algorithm and appropriate hyperparameters presents a challenge. Conventional Automated Machine Learning (AutoML) struggles to address this because the hold-out dataset lacks labels and its loss doesn’t reliably reflect anomaly detection accur...Show More

AutoKAD: Empowering KPI Anomaly Detection with Label-Free Deployment

Zhaoyang Yu;Changhua Pei;Shenglin Zhang;Xidao Wen;Jianhui Li;Gaogang Xie;Dan Pei

Year: 2023 | Conference Paper |

A Multi-Agent Framework for Recommendation with Heterogeneous Sources

Yabin Zhang;Weiqi Shao;Xu Chen;Yali Du;Xiaoxiao Xu;Dong Zheng;Changhua Pei;Shuai Zhang;Peng Jiang;Kun Gai

2023 International Joint Conference on Neural Networks (IJCNN)
Year: 2023 | Conference Paper |
With the ever prospering of the web technologies, there is a common need to make recommendations from heterogeneous sources, such as recommending products and advertisements together on the e-commerce websites. People usually solve such recommendation problem by a two-stage paradigm, where the first stage is generating candidates from each source, and the second one is aggregating and ranking the ...Show More

A Multi-Agent Framework for Recommendation with Heterogeneous Sources

Yabin Zhang;Weiqi Shao;Xu Chen;Yali Du;Xiaoxiao Xu;Dong Zheng;Changhua Pei;Shuai Zhang;Peng Jiang;Kun Gai

Year: 2023 | Conference Paper |

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