Towards traffic matrix prediction with LSTM recurrent neural networks. Issue 9 (1st May 2018)

• Record Type: Journal Article
• Authors: Zhao, Jianlong; Qu, Hua; Zhao, Jihong; Jiang, Dingchao
• Is Part Of: Electronics letters. Volume 54:Issue 9(2018)
• Page Start: 566
• Page End: 568
• Publication Date: 2018-05-01
• Subjects: regression analysis -- recurrent neural nets -- telecommunication traffic -- telecommunication network management -- telecommunication computing; traffic matrix prediction -- LSTM recurrent neural networks -- network management tasks -- large‐scale networks -- network traffic -- deep LSTM RNNs -- Abilene network -- TM estimation -- TM prediction performance -- spatiotemporal features -- linear regression model; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0336
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : This Letter investigates traffic matrix (TM) prediction that is widely used in various network management tasks. To fastly and accurately attain timely TM estimation in large‐scale networks, the authors propose a deep architecture based on LSTM recurrent neural networks (RNNs) to model the spatio‐temporal features of network traffic and then propose a novel TM prediction approach based on deep LSTM RNNs and a linear regression model. By training and validating it on real‐world data from Abilene network, the authors show that the proposed TM prediction approach can achieve state‐of‐the‐art TM prediction performance.