Authors: Sukant Kishoro Bisoy; Prasant Kumar Pattnaik
Addresses: Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha-752054, India ' School of Computer Engineering, KIIT University, Patia, Bhubaneswar, Odisha-751024, India
Abstract: To deal with nonlinear and complex problems of internet congestion control, an intelligent scheme is required, which can learn the traffic pattern of the network. In this paper, we design a robust AQM scheme called neuron-based AQM (N-AQM) to efficiently control the complex network congestion problem and achieve QoS. In N-AQM, a neural network is used to predict the future value of current queue length and estimate the differential queue length error and use it to define the packet drop probability. Our simulation result demonstrates that N-AQM is stable, robust and outperforms other AQM schemes. From the result section, it is observed that N-AQM is more efficient in stabilising the queue length around the target with faster settling time and incurs lower oscillation than others.
Keywords: active queue management; AQM; neuron; quality of service; QoS; stability; robustness.
International Journal of Reasoning-based Intelligent Systems, 2020 Vol.12 No.4, pp.238 - 247
Accepted: 11 Jan 2020
Published online: 03 Dec 2020 *