AIOpsArena: Scenario-Oriented Evaluation and Leaderboard for AIOps Algorithms in Microservices

作者：Yongqian Sun, Jiaju Wang, Zhengdan Li, Xiaohui Nie, Minghua Ma, Shenglin Zhang, Yuhe Ji, Lu Zhang, Long Wen, H. F. Chen, Yongnan Luo, Dan Pei · 年份：2025 · DOI：10.1109/saner64311.2025.00082 · 被引用次数：3 · 研究领域：Software System Performance and Reliability、Cloud Computing and Resource Management、IoT and Edge/Fog Computing

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.