面向场景的微服务管理AIOps算法评估基准
A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management
AI总结：
针对现有AIOps基准依赖离线数据、场景静态的问题，提出面向场景的实时微服务基准MicroServo，支持动态数据集与场景模拟，并验证其在三个典型场景中的有效性。
摘要：
AIOps算法在微服务系统的维护中扮演着至关重要的角色。以往许多基准的性能排行榜为选择合适的算法提供了有价值的指导。然而，现有的AIOps基准主要利用离线数据集来评估算法，无法持续地使用实时数据集评估算法性能，且评估的操作场景是静态的，这不足以支持有效的算法选择。为解决这些问题，我们提出了一个评估一致且面向场景的评估框架，名为MicroServo。其核心思想是构建一个实时微服务基准，以生成实时数据集，并在此基础上一致地模拟特定的操作场景。MicroServo通过根据操作场景选择特定的算法和数据集来支持不同的排行榜，还支持部署各种类型的算法，实现算法的热插拔。最后，我们使用三个典型的微服务操作场景对MicroServo进行了测试，以证明其效率和可用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
