当前位置: X-MOL 学术Softw. Pract. Exp. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Network traffic prediction method based on echo state network with adaptive reservoir
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-12-28 , DOI: 10.1002/spe.2950
Jian Zhou 1, 2 , Haoming Wang 1, 2 , Fu Xiao 1, 2 , Xiaoyong Yan 1, 2 , Lijuan Sun 1, 2
Affiliation  

Network traffic prediction is of great significance to resource management in cyber-physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short-term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self-similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN-AR). First, the framework of ESN-AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN-AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN-AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state-of-the-art models, the proposed method achieves more accurate and stable prediction performance.

中文翻译:

基于回波状态网络的自适应水库网络流量预测方法

网络流量预测对于网络物理系统(CPS)中的资源管理具有重要意义。特别是,网络流量是一个非线性时间序列。回声状态网络(ESN)是一种新型的神经网络,具有很强的非线性处理能力和短期记忆能力,因此在预测非线性时间序列方面可以取得良好的性能。然而,网络流量具有自相似性、混沌性、可变性等各种特性。作为ESN的核心,储库一旦产生就固定不变,限制了ESN在不同网络流量下的预测性能。为实现普遍优异的预测性能,本文提出了一种新的基于ESN自适应水库的网络流量预测方法(ESN-AR)。第一的,构建ESN-AR框架用于网络流量预测,其中将生成对抗网络(GAN)的思想融入ESN以自适应调整水库。具体来说,ESN用作生成模型来预测网络流量,前馈神经网络(FNN)用作判别模型来区分真实网络流量和预测网络流量。其次,提出了 ESN-AR 的对抗训练算法,根据网络流量特征获得合适的水库。最后,将ESN-AR应用于三种不同特性的实际网络流量的预测。仿真结果表明,与最先进的模型相比,所提出的方法实现了更准确和稳定的预测性能。其中将生成对抗网络 (GAN) 的思想融入 ESN 以自适应调整储层。具体来说,ESN用作生成模型来预测网络流量,前馈神经网络(FNN)用作判别模型来区分真实网络流量和预测网络流量。其次,提出了 ESN-AR 的对抗训练算法,根据网络流量特征获得合适的水库。最后,将ESN-AR应用于三种不同特性的实际网络流量的预测。仿真结果表明,与最先进的模型相比,所提出的方法实现了更准确和稳定的预测性能。其中将生成对抗网络 (GAN) 的思想融入 ESN 以自适应调整储层。具体来说,ESN用作生成模型来预测网络流量,前馈神经网络(FNN)用作判别模型来区分真实网络流量和预测网络流量。其次,提出了 ESN-AR 的对抗训练算法,根据网络流量特征获得合适的水库。最后,将ESN-AR应用于三种不同特性的实际网络流量的预测。仿真结果表明,与最先进的模型相比,所提出的方法实现了更准确和稳定的预测性能。
更新日期:2020-12-28
down
wechat
bug