An efficient algorithm for modelling and dynamic prediction of network traffic

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 fail, this paper proposes one algorithm based on product form results (PRF) for fractionally auto regressive integrated moving average (FARIMA) model, namely PFRF. In this algorithm, the prediction method is established by FARIMA model, through equations for queuing state 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, not only it has good adaptability, but has 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.