It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics. In this paper, we pro- pose a machine-learning approach to derive performance models from data. We focus on queuing networks, and crucially exploit a deterministic approximation of their average dynamics in terms of a compact system of ordinary differential equations. We encode these equations into a recurrent neural network whose weights can be directly related to model parameters. This allows for an inter- pretable structure of the neural network, which can be trained from system measurements to yield a white-box parameterized model that can be used for prediction purposes such as what-if analyses and capacity planning. Using synthetic models as well as a real case study of a load-balancing system, we show the effectiveness of our technique in yielding models with high predictive power.
|Titolo:||Learning queuing networks by recurrent neural networks|
GARBI, GIULIO (Corresponding)
Incerto, Emilio (Corresponding)
TRIBASTONE, MIRCO (Corresponding)
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|