Towards Describing Multi-fractality of Traffic Using Local Hurst Function

Authors:
Ming Li;S. C. Lim;Bai-Jiong Hu;Huamin Feng
Affiliations:
School of Information Science & Technology, East China Normal University, Shanghai 200062, P.R. China;Faculty of Engineering, Multimedia University, 63100 Cyberjaya, Selanger, Malaysia;School of Information Science & Technology, East China Normal University, Shanghai 200062, P.R. China;Key Laboratory of Security and Secrecy of Information, Beijing Electronic Science and Technology Institute, Beijing 100070, P.R. China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Year:
2007

Citing 7
Cited 1

Wide area traffic: the failure of Poisson modeling

IEEE/ACM Transactions on Networking (TON)
On self-similar traffic in ATM queues: definitions, overflow probability bound, and cell delay distribution

IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes

IEEE/ACM Transactions on Networking (TON)
The changing nature of network traffic: scaling phenomena

ACM SIGCOMM Computer Communication Review
Difficulties in simulating the internet

IEEE/ACM Transactions on Networking (TON)
Effective bandwidth for a single server queueing system with fractional Brownian input

Performance Evaluation - Long range dependence and heavy tail distributions
Self-similar processes in communications networks

IEEE Transactions on Information Theory

A practical method for weak stationarity test of network traffic with long-range dependence

MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Long-range dependence and self-similarity are two basic properties of network traffic time series. Fractional Brownian motion (fBm) and its increment process fractional Gaussian noise (fGn) are commonly used to model network traffic with the Hurst index H that determines both the regularity of the sample paths and the long memory property of traffic. However, it appears too restrictive for traffic modeling since it can only model sample paths with the same smoothness for all time parameterized by a constant H. A natural extension of fBm is multifractional Brownian motion (mBm), which is indexed by a time-dependent Hurst index H(t). The main objective of this paper is to model multi-fractality of traffic using H(t), i.e., mBm, on a point-by-point basis instead of an interval-by-interval basis as traditionally done in computer networks. The numerical results for H(t) of real traffic, which are demonstrated in this paper, show that H(t) of traffic is time-dependent, which not only provide an alternative evidence of the multifractal phenomena of traffic but also reveal an challenging issue in traffic modeling: multi-fractality modeling of traffic.