Evaluating Non-Hierarchical Overflow Loss Systems Using Teletraffic Theory and Neural Networks
| Autor: | Chi-Sing Leung, Yin-Chi Chan, Eric Wong |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Stochastic modelling Computer science Approximation algorithm 020206 networking & telecommunications 02 engineering and technology Blocking (statistics) Base (topology) Computer Science Applications Approximation error Modeling and Simulation Server 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm Information exchange |
| Zdroj: | IEEE Communications Letters. 25:1486-1490 |
| ISSN: | 2373-7891 1089-7798 |
| DOI: | 10.1109/lcomm.2021.3052683 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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