一句话：提出 ScalerEval，一个面向微服务自动扩缩器的端到端自动化、一致性评估测试床。
自动扩缩通过动态分配资源来应对微服务负载波动，但评估自动扩缩器十分耗时费力：研究者需实现大量基础接口，执行复杂手工操作，并依赖深入领域知识；频繁人工干预还会引入操作错误，导致不同自动扩缩器的评估结果不一致。为此，作者提出 ScalerEval，一个端到端、自动化且一致的自动扩缩器评估测试床。它集成了实现自动扩缩器所需的基础接口，并编排完整的评估工作流，以降低人工负担、提高评估一致性。论文公开了源代码与演示视频，但摘要未报告量化实验结果。
Auto-scaling is an automated approach that dynamically provisions resources for microservices to accommodate fluctuating workloads. Despite the introduction of many sophisticated auto-scaling algorithms, evaluating auto-scalers remains time-consuming and labor-intensive, as it requires the implementation of numerous fundamental interfaces, complex manual operations, and in-depth domain knowledge. Besides, frequent human intervention can inevitably introduce operational errors, leading to inconsistencies in the evaluation of different auto-scalers. To solve these issues, we present ScalerEval, an end-to-end automated and consistent testbed for auto-scalers. ScalerEval integrates essential fundamental interfaces for implementation of auto-scalers and further orchestrates a complete evaluation workflow for researchers. The source code is publicly available at https://github.com/WHU-AISE/ScalerEval and the demo video is available at https://youtu.be/HFxuXxNBTAM.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2504-08308,
title = {ScalerEval: Automated and Consistent Evaluation Testbed for Auto-scalers in Microservices},
author = {Shuaiyu Xie and Yang Luo and Yuzhen Tan and Yunqing Yong and Xiaosong Huang and Jian Wang and Bing Li},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806415}
}