To efficiently manage resources and provide guaranteed services, today's computing systems monitor and collect a large number of resource usages, for example the average and time series of CPU utilization. However, little is known about the analytical distribution of resource usages, which are the crucial parameters to infer performance metrics defined in service level agreements (SLAs), such as response times and throughputs. In this paper, we aim to characterize the entire distribution of CPU utilization via stochastic reward models. In particular, we first study and derive the probability density function of the utilization of widely known and applied queuing systems, namely Poisson processes, Markov modulated Poisson processes and time-varying Poisson processes. Secondly, we apply our proposed analysis on characterizing the CPU usage of live production systems, and simulated queuing systems. Evaluation results show that analytical characterization of the selected queueing models can capture the utilization distribution of a wide range of real-life systems well, and we argue the robustness of our methodology to further infer system performance metrics. © 2013 IEEE.