Evaluation of the Performance Parameters of a Closed Queuing Network Using Artificial Neural Networks

Gorbunova, A. V.; Vishnevsky, Vladimir

doi:10.1007/978-3-030-92507-9_22

Cited by 3 publications

(1 citation statement)

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“…The combination of the Markov decision problem and neural networks for the heterogeneous queueing model with process sharing was studied by Efrosinin et al [ 9 ]. The performance parameters of the closed queueing network by means of a neural network were evaluated in Gorbunova and Vishnevsky [ 10 ]. In addition to the presented results of using neural networks in hypothetical queueing theory models, academic studies in this area with real-world applications have gradually been proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling in General Multi-Queue System by Combining Simulation and Neural Network Techniques

Efrosinin

Vishnevsky

Stepanova³

2023

Sensors

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The problem of optimal scheduling in a system with parallel queues and a single server has been extensively studied in queueing theory. However, such systems have mostly been analysed by assuming homogeneous attributes of arrival and service processes, or Markov queueing models were usually assumed in heterogeneous cases. The calculation of the optimal scheduling policy in such a queueing system with switching costs and arbitrary inter-arrival and service time distributions is not a trivial task. In this paper, we propose to combine simulation and neural network techniques to solve this problem. The scheduling in this system is performed by means of a neural network informing the controller at a service completion epoch on a queue index which has to be serviced next. We adapt the simulated annealing algorithm to optimize the weights and the biases of the multi-layer neural network initially trained on some arbitrary heuristic control policy with the aim to minimize the average cost function which in turn can be calculated only via simulation. To verify the quality of the obtained optimal solutions, the optimal scheduling policy was calculated by solving a Markov decision problem formulated for the corresponding Markovian counterpart. The results of numerical analysis show the effectiveness of this approach to find the optimal deterministic control policy for the routing, scheduling or resource allocation in general queueing systems. Moreover, a comparison of the results obtained for different distributions illustrates statistical insensitivity of the optimal scheduling policy to the shape of inter-arrival and service time distributions for the same first moments.

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Section: Introductionmentioning

confidence: 99%