Abstract: QPLEX is a nonstationary, nonparametric, non-Markovian modeling and analysis paradigm for stochastic networks. QPLEX generates transient distributions of key performance metrics, such as the number of customers and the virtual waiting time at each station at all times. From a modeling perspective, QPLEX is quite versatile: for example, it can accommodate time-varying arrival processes, arbitrary time-varying service-time distributions, time-varying server counts, abandonments, balking, probabilistic routing and capacity/routing policies based on cycle-time distributions.
In Part I of this talk, we will present a proof-of-concept that is remarkably accurate, widely applicable, and extremely fast. We will also describe the mechanisms underlying the QPLEX calculus.
In Part II of this talk, we will discuss the principles of QPLEX modeling. We will also discuss computational opportunities, future directions and prospective application areas.

Joint work with Steve Hackman (Georgia Tech).