Authors: Wenjie Fan; Hong Zhang; Kuan-Ching Li; Shunxiang Zhang; Mario Donato Marino; Hai Jiang
Addresses: College of Information Science and Engineering, Chengdu University, Chengdu, 610106, China; Key Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu, 610106, China ' College of Information Science and Engineering, Chengdu University, Chengdu, 610106, China; Key Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu, 610106, China ' Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung 43301, Taiwan; College of Computer, Hubei University of Education, Wuhan 430205, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China ' School of Computing, Creative Technologies and Engineering, Leeds Beckett University, UK ' Department of Computer Science, Arkansas State University, USA
Abstract: 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.
Keywords: prediction; product form results; PRF; FARIMA model; average length of queue.
International Journal of Computational Science and Engineering, 2018 Vol.16 No.3, pp.311 - 320
Received: 14 Oct 2016
Accepted: 02 Nov 2016
Published online: 03 May 2018 *