An efficient algorithm for modelling and dynamic prediction of network traffic

作者：Fan, Wenjie; Zhang, Hong; Li, Kuan-Ching^*; Zhang, Shunxiang; Marino, Mario Donato; Jiang, Hai

来源：International Journal of Computational Science and Engineering, 2018, 16(3): 311-320.

DOI：10.1504/IJCSE.2018.091779

• Auto-regressive integrated moving average
• Average length
• Average queue lengths
• Experimental simulations
• f-ARIMA models
• Prediction accuracy
• Product forms
• Standard deviation

Network node degradation is an important problem in internet of things given the ubiquitous high number of personal computers, tablets, phones and other equipments present nowadays. In order to verify the network traffic degradation as one or multiple nodes in a network failure, this paper proposes an algorithm based on product form results (PRF) for fractionally autoregressive integrated moving average (FARIMA) model, namely PFRF. In this algorithm, the prediction method is established by FARIMA model, through equations for queuing situation and average queue length in steady state derived from queuing theory. Experimental simulations were conducted to investigate the relationships between average queue length and service rate. Results demonstrated that it not only has good adaptability, but also achieved promising magnitude of 9.87 as standard deviation which shows its high prediction accuracy, given the low-magnitude difference between original value and the algorithm.

• 出版日期2018
• 单位安徽大学