Evaluating Non-Hierarchical Overflow Loss Systems Using Teletraffic Theory and Neural Networks
Published in IEEE Communications Letters, 2021-01-18
Proposes hybrid learning combining the Information Exchange Surrogate Approximation and neural networks for blocking probability evaluation in non-hierarchical overflow loss systems.
Abstract: The Information Exchange Surrogate Approximation (IESA) is a powerful tool for estimating the blocking probability of non-hierarchical overflow loss systems (NH-OLSs), but can exhibit significant approximation errors in some cases. This letter proposes a new method of evaluating the blocking probability of generic NH-OLSs by combining machine learning with IESA. Specifically, we modify IESA by using neural networks (NN) to tune a newly introduced parameter in the IESA algorithm. Extensive numerical results for a simple NH-OLS show that our new hybrid method, which we call IESA+NN, is more accurate and robust than both base IESA and direct NN-based approximation of NH-OLS blocking probability, while remaining much more computationally efficient than computer simulation. Furthermore, due to the generic nature of our technique, IESA+NN is also easily extensible to more specialized stochastic models for communications and service systems, where base IESA has previously been applied.
DOI: 10.1109/LCOMM.2021.3052683
Recommended citation:
Y.-C. Chan, E. W. M. Wong, and C. S. Leung, “Evaluating non-hierarchical overflow loss systems using teletraffic theory and neural networks,” IEEE Communications Letters, vol. 25, pp. 1486–1490, May 2021.