  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.
