使用神经网络理解计算机网络延迟建模
Understanding the Modeling of Computer Network Delays using Neural Networks
摘要：
网络的最新趋势是提出使用机器学习（ML）技术来控制和操作网络。在这种情况下，ML可以用作计算机网络建模技术来构建估计网络性能的模型。实际上，网络建模是许多网络功能的核心技术，例如在优化领域，其中模型用于搜索满足目标策略的配置。在本文中，我们的目标是提供一个答案，以下列问题：神经网络可以准确地模拟延迟的计算机网络作为输入流量的函数？为此，我们假设网络是一个黑盒子，它的输入是一个流量矩阵，输出是延迟。然后，我们训练不同的神经网络模型，并评估其准确性在不同的基本网络特征：拓扑结构，规模，流量强度和路由。有了这个，我们的目标是有一个更好的理解计算机网络建模与神经网络，并最终提供实用的指导方针，如何需要这样的模型进行训练。
英文摘要：
Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
