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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Long Wen, Zhengdan Li, Yongqian Sun, Yuhe Ji et al.
2 citations
Software System Performance and Reliability
2.97
article

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

Long Wen, Zhengdan Li, Yongqian Sun, Yuhe Ji, Dan Pei, H. F. Chen, Lu Zhang, Xiaohui Nie, Jiaju Wang, Y. Luo, Minghua Ma, Shenglin Zhang
article en
2 citations

Abstract

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.

Nankai University (CN), Microsoft Research (United Kingdom) (GB), Tsinghua University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Software System Performance and Reliability
2.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AIOpsArena: Scenario-Oriented Evaluation and Leaderboard for AIOps Algorithms in Microservices — Long Wen, Zhengdan Li, et al. · (2025) | TGRS Research Map | TGRS