A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic

Huo, Liuwei; Jiang, Dingde; Zhu, Xiangnan; Wang, Yuqing; Lv, Zhihan; Singh, Surjit

doi:10.1002/dac.4092

Cited by 58 publications

(10 citation statements)

References 26 publications

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“…However, not much work has been done towards accurately spelling out actual traffic flows throughout a given network. e authors in [2,3] propose and evaluate an algorithm for obtaining a detailed end-to-end traffic matrix of a given network. eir proposed algorithm is based on fractal and cubic spline interpolation reconstruction approaches.…”

Section: Related Work On Contention Resolutionmentioning

confidence: 99%

A Node-Regulated Deflection Routing Framework for Contention Minimization

Nleya

Mutsvangwa

2020

Journal of Computer Networks and Communications

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Optical Burst Switching (OBS) paradigm coupled with Dense Wavelength Division Multiplexing (DWDM) has become a practical candidate solution for the next-generation optical backbone networks. In its practical deployment only the edge nodes are provisioned with buffering capabilities, whereas all interior (core) nodes remain buffer-less. In that way the implementation becomes quite simple as well as cost effective as there will be no need for optical buffers in the interior. However, the buffer-less nature of the interior nodes makes such networks prone to data burst contention occurrences that lead to a degradation in overall network performance as a result of sporadic heavy burst losses. Such drawbacks can be partly countered by appropriately dimensioning available network resources and reactively by way of deflecting excess as well as contending data bursts to available least-cost alternate paths. However, the deflected data bursts (traffic) must not cause network performance degradations in the deflection routes. Because minimizing contention occurrences is key to provisioning a consistent Quality of Service (QoS), we therefore in this paper propose and analyze a framework (scheme) that seeks to intelligently deflect traffic in the core network such that QoS degradations caused by contention occurrences are minimized. This is by way of regulated deflection routing (rDr) in which neural network agents are utilized in reinforcing the deflection route choices at core nodes. The framework primarily relies on both reactive and proactive regulated deflection routing approaches in order to prevent or resolve data burst contentions. Simulation results show that the scheme does effectively improve overall network performance when compared with existing contention resolution approaches. Notably, the scheme minimizes burst losses, end-to-end delays, frequency of contention occurrences, and burst deflections.

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Section: Related Work On Contention Resolutionmentioning

confidence: 99%

A Node-Regulated Deflection Routing Framework for Contention Minimization

Nleya

Mutsvangwa

2020

Journal of Computer Networks and Communications

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“…Based on the low rank and time-varying characteristics of traffic flows, the authors in [24] proposed a novel mechanism for the on-line network traffic measurement by utilizing the flow measurement ability of the SDN technique. In [25], an SDN-based fine-grained measurement method in vehicular communication network was proposed. The proposed method contained two phases.…”

Section: Related Workmentioning

confidence: 99%

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

et al. 2020

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The end-to-end network traffic information is the basis of network management for a large-scale intelligent transportation systems-oriented backbone network. To obtain exact network traffic data, a prevalent idea is to deploy NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, but also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q-learning-based approach to deal with the problem of the interface-selection. We propose an approach to compute the reward, furthermore a modified Q-learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GÉANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly. INDEX TERMS Network measurement, reinforcement learning, intelligent transportation systems, IP backbone network.

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“…It is very important to effectively manage, control, and improve the service of the network 3 . Network traffic is a very important parameter to evaluate network load and its working state 4–6 . Network administrators can grasp the current status of the network by monitoring the network traffic.…”

Section: Introductionmentioning

confidence: 99%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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Summary Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.

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A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic

Cited by 58 publications

References 26 publications

A Node-Regulated Deflection Routing Framework for Contention Minimization

A Node-Regulated Deflection Routing Framework for Contention Minimization

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

Network traffic prediction method based on wavelet transform and multiple models fusion

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