Machine learning based Call Admission Control approaches: A comparative study
Machine learning based Call Admission Control approaches: A comparative study
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DOI：
10.1109/cnsm.2010.5691261
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
A. Bashar;G. Parr;S. McClean;B. Scotney;D. Nauck
中科院分区：
文献类型：
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作者：
A. Bashar;G. Parr;S. McClean;B. Scotney;D. Nauck
提供有保证的服务质量（QoS）的重要性怎么强调都不过分，特别是在支持公共IP传输网络上的融合服务的NGN环境中。呼叫接纳控制（CAC）机制确实以主动的方式为基于类别的服务提供QoS。然而，由于下一代网络的复杂性，规模和动态性的因素，机器学习技术的分析方法，提供自主CAC青睐。本文是一个努力，比较两个这样的方法-神经网络（NN）和贝叶斯网络（BN）的性能，网络行为建模和估计的QoS指标中使用的CAC算法。它提供了一种找到最佳模型训练大小以进行准确预测的方法。性能比较是基于通过Opnet中的模拟网络进行的广泛实验。这项比较研究的结果为NN和BN模型的行为以及如何利用它们实现更好的CAC提供了一些有趣的见解。
The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches - Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.