A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management

Yongqian Sun, Jiaju Wang, Zhengdan Li, Xiaohui Nie, Minghua Ma, Shenglin Zhang, Yuhe Ji, Lu Zhang, Wen Long, Hengmao Chen, Yongnan Luo, Dan Pei

AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline 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 MicroServo. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. MicroServo 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 MicroServo with three typical microservice operation scenarios to demonstrate its efficiency and usability.