Network traffic prediction method based on echo state network with adaptive reservoir. (28th December 2020)

• Record Type: Journal Article
• Authors: Zhou, Jian; Wang, Haoming; Xiao, Fu; Yan, Xiaoyong; Sun, Lijuan
• Other Names: Zhou Junlong guestEditor.; Kritikakou Angeliki guestEditor.; Zhu Dakai guestEditor.; Lastra Jose L. Martinez guestEditor.; Hu Shiyan guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 11(2021)
• Page Start: 2238
• Page End: 2251
• Publication Date: 2020-12-28
• Subjects: adaptive reservoir -- cyber‐physical systems -- echo state network -- generative adversarial network -- network traffic prediction; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2950
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: 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.