AIOpsArena: Scenario-Oriented Evaluation and Leaderboard for AIOps Algorithms in Microservices
AIOps algorithms playa crucial role in the mainte-nance of microservice systems. Many previous benchmarks' per-formance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline static datasets to evaluate algorithms. They cannot consistently evaluate the performance of algorithms using real-time datasets, and the operation scenarios for evaluation are static, which is insufficient for effective algorithm selection. To address these issues, we propose an evaluation-consistent and scenario-oriented evaluation framework named AIOpsArena. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. AIOpsArena supports different leaderboards by selecting specific algorithms and datasets according to the operation scenarios. It also supports the deployment of various types of algorithms, enabling algorithms hot-plugging. At last, we test AIOpsArena with typical microservice operation scenarios to demonstrate its efficiency and usability. Platform and a video demonstrating the functioning of AIOpsArena is available from https://github.com/AIOpsArena/aiopsarena.
Authors
- Long Wen (ORCID: https://orcid.org/0000-0002-8355-9947)
- Zhengdan Li (ORCID: https://orcid.org/0009-0004-9409-9909)
- Yongqian Sun (ORCID: https://orcid.org/0000-0003-0266-7899)
- Yuhe Ji (ORCID: https://orcid.org/0000-0002-9115-9863)
- Dan Pei (ORCID: https://orcid.org/0000-0002-5113-838X)
- H. F. Chen
- Lu Zhang (ORCID: https://orcid.org/0000-0001-6829-703X)
- Xiaohui Nie (ORCID: https://orcid.org/0000-0002-0371-854X)
- Jiaju Wang (ORCID: https://orcid.org/0009-0007-7236-9455)
- Y. Luo (ORCID: https://orcid.org/0009-0001-8755-2937)
- Minghua Ma
- Shenglin Zhang
Institutions
- Nankai University (CN)
- Microsoft Research (United Kingdom) (GB)
- Tsinghua University (CN)
Publication Details
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1109/saner64311.2025.00082
- Citations
- 2
- Primary Topic
- Software System Performance and Reliability
- Type
- article
- Field-Weighted Citation Impact
- 2.97