LN: a meta-solver for layered queueing network analysis

We overview LN, a novel solver introduced in the LINE soft- ware package to analyze layered queueing network (LQN) models. The novelty of the LN solver lies in its capability to analyze LQNs with a user- defined combination of solution paradigms, including discrete-event and stochastic simulation, continuous-time Markov chain analysis (CTMC), normalizing constant evaluation (NC), matrix analytic methods (MAM), mean-field approximations (FLUID), and mean-value analysis (MVA). Being parametric in the solver used for each LQN layer, LN as a whole enables the efficient computation of advanced performance metrics such as marginal and joint state probabilities, response and passage time distributions, and transient measures, leveraging individual strengths of the supported solution paradigms. We discuss in particular recent developments added to NC, the default layer solver of LN, which significantly improve the solution of queueing network models obtained using loose layerin