2025/04/11 by Xie, Shuaiyu, Wang, Jian, Luo, Yang +3
#FOS: Computer and information sciences #Software Engineering (cs.SE)
paper · doi:10.48550/arxiv.2504.08308
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 address these issues, we present ScalerEval, an end-to-end automated and consistent testbed for auto-scalers in microservices. ScalerEval integrates essential fundamental interfaces for implementation of auto-scalers and further orchestrates a one-click evaluation workflow for researchers. The source code is publicly available at \hrefhttps://github.com/WHU-AISE/ScalerEvalhttps://github.com/WHU-AISE/ScalerEval.