Learning Queuing Networks via Linear Optimization

The automatic derivation of analytical performance models is an essential tool to promote a wider adoption of performance engineering techniques in practice. Unfortunately, despite the importance of such techniques, the attempts pursuing that goal in the literature either focus on the estimation of service demand parameters only or suffer from scalability issues and sub-optimality due to the intrinsic complexity of the underlying optimization methods. In this paper, we propose an efficient linear programming approach that allows to derive queuing network (QN) models from sampled execution traces. For doing so, we rely on a deterministic approximation of the average dynamic of QNs in terms of a compact system of ordinary differential equations. We encode these equations into a linear optimization problem whose decision variables can be directly related to the unknown QN parameters, i.e., service demands and routing probabilities. Using models of increasing complexity, we show the efficiency and the effectiveness of our technique in yielding models with high prediction power.