A Time-Varying FARIMA Model for Internet Traffic

Song, Li; Bondon, Pascal; Cao, Yang; Cheng, Qi

doi:10.1109/cisp.2008.719

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Among the processes possessing such property are FARIMA discrete processes and multifractals. Let us recall that FARIMA models have been developed to generate time series with long-range dependence [48,49] and have been applied to model experimental data in fields such as hydrology [50,51], ocean science [52], meteorology [53], solar physics [54] or traffic network [55,56]; see also [57,58] for numerical works. We can thus see that multifractals and FARIMA models are two amongst the realistic and popular alternative models applied for understanding and assessing the long-range properties of time series.…”

Section: Discussionmentioning

confidence: 99%

A discrete log-normal process to sequentially generate a multifractal time series

Perpète¹,

Schmitt²

2011

J. Stat. Mech.

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We propose a discrete log-normal process to generate an intermittent time series and show that it possesses multifractal properties. With such a process, it is possible for the first time to sequentially generate a multifractal time series with an arbitrary length and a specified and fixed scale ratio. We explore its predictability properties and discuss a log-stable generalization. Since this corresponds to a special type of log-FARIMA truncated process, this model provides for the first time a link between FARIMA and multifractal families of models.

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Section: Discussionmentioning

confidence: 99%

A discrete log-normal process to sequentially generate a multifractal time series

Perpète¹,

Schmitt²

2011

J. Stat. Mech.

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“…In recent years, terrestrial network‐based traffic prediction methods have received extensive attention from researchers. Traditional network traffic prediction methods, such as autoregressive integrated moving average (ARIMA) model, 20 fractional autoregressive integrated moving average (FARIMA) model, 21 and wavelet packet‐autocorrelation Function, 22 have achieved good prediction performance in the field of traffic prediction for terrestrial networks.…”

Section: Related Workmentioning

confidence: 99%

Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

Luo,

Ning,

Chen

et al. 2023

Trans Emerging Tel Tech

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Uneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

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“…However, previous models, including Markov process, Poisson process, autoregressive (AR) model and moving average (ARMA) model and so on, can only describe the data of short-range dependence (SRD) [3]. Yet, self-similar models such as fractional gaussian noise (FGN) model and fractional Brownian motion (FBM) model have been developed to resolve this problem.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, these models are incapable of giving a description of SRD, which is also a significant property of the network traffic. Fractional autoregressive integrated moving average (FARIMA) model is provided by this work to reveal the co-existence of both SRD and LRD in the network flow [3], and the model also has a wide range of applications in other domains. Dr Kristoufek found that the Hurst exponent he got from the time series he previously generated had large deviation to the expected one which was used to generate the time series [1].…”

Section: Introductionmentioning

confidence: 99%