Simulating the workload is an essential procedure in microservice systems as it helps augment realistic workloads whilst safeguarding user privacy. The efficacy of such simulation depends on its dynamic assessment. The straightforward and most efficient approach to this is comparing the original workload with the simulated one using Key Performance Indicators (KPIs), which capture the state of the system. Nonetheless, due to the extensive volume and complexity of KPIs, fully evaluating them is not feasible, and measuring their similarity poses a significant challenge. This paper introduces a similarity metric algorithm for KPIs, the Extended Shape-Based Distance (ESBD), which gauges similarity in both shape and intensity. Additionally, we propose a KPI-based Evaluation Framework for Workload Simulations (KEWS), comprising three modules: preprocessing, compression, and evaluation. These methodologies effectively counteract the adverse effects of KPIs' characteristics and offer a holistic evaluation. Experimental results substantiate the effectiveness of both ESBD and KEWS.

翻译：工作负载仿真是微服务系统中的关键流程，有助于增强真实负载的同时保护用户隐私。此类仿真的有效性取决于其动态评估。最直接有效的方法是利用捕获系统状态的关键性能指标（KPIs），将原始工作负载与仿真工作负载进行对比。然而，由于KPIs数量庞大且复杂性高，全面评估难以实现，且其相似性测量构成重大挑战。本文提出一种KPI相似性度量算法——扩展形基于距离（ESBD），可从形态与强度两个维度衡量相似性。同时，我们构建了基于KPIs的工作负载仿真评估框架（KEWS），包含预处理、压缩和评估三个模块。这些方法有效抵消了KPIs特性带来的负面影响，实现了全局性评估。实验结果证实了ESBD与KEWS的有效性。