On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
A hidden semi-Markov model for web workload self-similarity
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
Markov models of internet traffic and a new hierarchical MMPP model
Computer Communications
Internet traffic source based on hidden Markov model
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
A multifractal wavelet model with application to network traffic
IEEE Transactions on Information Theory
Traffic models in broadband networks
IEEE Communications Magazine
Traffic modeling for telecommunications networks
IEEE Communications Magazine
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
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It is well known that network traffic presents obvious periodicity due to the human reason. Conventional research only focuses on characterizing the periodicity, but ignores the details of the process of each cycle. In this paper, a new periodic structural model is proposed to describe the network traffic which is period and hierarchical. The proposed approach is based on the hidden Markov model and includes two latent Markov chains and one observable process. One of the latent Markov chains is called macro-state process which is used to describe the large-scale period trends of network traffic. The remaining latent Markov chain is called sub-state process which is used to describe the small-scale fluctuations that are happening within the duration of a given macro state. An efficient parameter re-estimation algorithm is derived for the model. Experiments based on real network traffic of a large-scale campus network are implemented to validate the proposed model.