Compact Markov-modulated models for multiclass trace fitting
提出首个标记马尔可夫调制泊松过程（M3PP）的计数过程拟合算法，并发明一种称为“插置”的组合方法，使多个两状态M3PP的叠加状态空间从指数增长变为线性增长，适用于多类型事件轨迹的拟合与仿真。
Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes.