Response Time Anomaly Diagnosis for Search Service

Xia Sibo; Ma Minghua; Jin Pengxiang; Cui Liyue; Zhang Shenglin; Jin Wa; Sun Yongqian; Pei Dan

doi:10.7544/issn1000-1239.202330054

Journal of Computer Research and Development > 2024 > 61(6): 1573-1584. > DOI: 10.7544/issn1000-1239.202330054 CSTR: 32373.14.issn1000-1239.202330054

Xia Sibo, Ma Minghua, Jin Pengxiang, Cui Liyue, Zhang Shenglin, Jin Wa, Sun Yongqian, Pei Dan. Response Time Anomaly Diagnosis for Search Service[J]. Journal of Computer Research and Development, 2024, 61(6): 1573-1584. DOI: 10.7544/issn1000-1239.202330054

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Response Time Anomaly Diagnosis for Search Service

1.
College of Software, Nankai University, Tianjin 300457
2.
Haihe Laboratory of Information Technology Application Innovation, Tianjin 300450
3.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084

Funds: This work was supported by the National Natural Science Foundation of China for Yong Scientists (61902200, 62072264) and the Natural Science Foundation of Tianjin (21JCQNJC00180).

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Author Bio:
Xia Sibo: born in 2000. Master candidate. His main research interests include knowledge graph, and failure detection and diagnosis

Ma Minghua: born in 1993. PhD. Researcher at Microsoft Research Lab Asia. His main research interests include cloud intelligence and AIOps

Jin Pengxiang: born in 1998. Master. His main research interests include failure diagnosis and root cause analysis

Cui Liyue: born in 1997. Master. Her main research interests include anomaly detection, failure diagnosis, and machine learning

Zhang Shenglin: born in 1989. PhD, associate professor. Member of CCF, IEEE, and ACM. His main research interests include failure detection, and diagnosis and prediction in data center networks

Jin Wa: born in 2001. Bachelor. Her main research interests include failure diagnosis and root cause analysis

Sun Yongqian: born in 1988. PhD, associate professor. Member of CCF, IEEE, and ACM. His main research interest includes anomaly detection, root cause analysis, and failure diagnosis in service management

Pei Dan: born in 1973. PhD, associate professor. Member of CCF. Senior member of IEEE and ACM. His main research interest includes network and service management
Received Date: January 29, 2023
Revised Date: July 24, 2023
Available Online: March 13, 2024

Graphical Abstract

Abstract

Abstract

The timely response of network services is crucial to improving user experience. Taking search engine as a typical example of network services, service providers need to ensure that the search response time is within one second. In practice, the search response time can be affected by many service attributes, such as user browsers, ISPs, and page loading methods. To optimize effectively, service providers need to identify the rules that cause high search response time, which are combinations of the above attributes. However, existing work encounters three challenges. First, the amount of search logs is large. Second, the search logs are unevenly distributed. Third, the rules with high generality are needed. Therefore, we propose a framework called Miner (multi-dimensional extraction of rules). Miner takes advantage of self-paced sampling to overcome the first and second challenges. To address the third challenge, Miner employs Corels to generate rules with high generality and recall. Our experiments use search logs provided from two top-tier search engine companies in China. The results show that Miner outperforms the state-of-the-art methods in terms of generality and recall. Operators adopt rules generated by Miner and optimize the performance of the search engine.
- quality of networking service,
- self-paced sampling,
- search engine,
- search response time,
- data mining

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References (14)

References

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