Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Chen, Bor‐Sen; Peng, Sen-Chueh; Wang, Ku-Chen

doi:10.1109/91.873574

Cited by 69 publications

(11 citation statements)

References 39 publications

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“…For example, fuzzy inference system was used to create a new air quality index while autoregressive model used to predict future air quality condition. The concept of fuzzy regression model was combined with the AR model to formulate the fuzzy AR model and applied in the forecasting [19][20][21]. Most of the researches deal with uncertainties in the AR model.…”

Section: Introductionmentioning

confidence: 99%

A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number

Lah

Arbaiy

2020

IJEECS

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<span>Data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by quantitative results. This also causes the forecasted model developed to be less precise because of the uncertainty contained in the input data used. Hence, preparing the data by means of handling inherent uncertainties is necessary to avoid the developed forecasting model to be less accurate. Traditional autoregressive (AR) model uses precise values and deals with the uncertainty normally in forecasting model. Fewer researches are focused on data preparation in time-series autoregressive for handling the uncertainties in data. Hence, this paper proposes a procedure to perform data preparation to handle uncertainty. The fuzzy data preparation involves the construction of fuzzy symmetric triangle numbers using percentage error and standard deviation method. The proposed approach is evaluated by using the simulation method for first-order autoregressive, AR (1) model in terms of forecasting accuracy performance. Simulation result demonstrates that the proposed approach obtains smaller error in forecasting and hence achieving better forecasting accuracy and dealing with uncertainty in the analysis.</span>

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Section: Introductionmentioning

confidence: 99%

A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number

Lah

Arbaiy

2020

IJEECS

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“…However, despite several developments delivering best-in class video quality over IP, especially in live video streaming, still presents a host of challenges. Significant amongst these challenges are network delays/jitters, video freezes and packet losses due to network congestion as studied by several researches [18][19][20][21]. The core problem of preventing downstream network congestion due to an increased traffic in proportion the number of users (live stream traffic increases proportionately with increase in user request) and consequently there is a reduction in available bandwidth overall.…”

Section: Introductionmentioning

confidence: 99%

A Learning Framework to Improve Video QoE in HTTP Adaptive Streaming SDN for Delivering Live Content

Natarajan

Krishnaveni

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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HTTP (Hyper Text Transport Protocol) adaptive streaming techniques suffers from start-up latencies and video stalls which impair Video Quality of Experience (QoE). We deal with these issues in 4 steps: i) we analyze the advantages of variable bit rate encoded streams on Adaptive Bit Rate (ABR) streaming and assess the impact of chunk sizes on video quality and scene complexity; ii) we propose a novel machine learning framework through a Gaussian Mixture model k-means clustering to isolate video stall events, the Machine Learning Pipeline runs these Support Vector Machines (SVM) algorithms to accurately predict the occurrence and duration of video stalls; iii) we employ Software Defined Networking (SDN) based OpenFlow controller to dynamically switch, re-prioritize tracks and chunk sizes across tracks to improve the overall Video QoE; iv) we further optimize this through an intelligent prediction of video stall occurrence and stall duration in accordance with network traffic conditions and scene complexity without compromising video quality.

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“…A number of studies of network traffic measurements have convincingly demonstrated that the network traffic time series is self-similar or long-range dependent(LRD) in nature. Since the network traffic time series represents nonlinear processes, the traditional linear time series models cannot explain and capture self-similarity and LRD features of the traffic [5].…”

Section: Introductionmentioning

confidence: 99%

A Network Traffic Prediction Model Based on Quantum Inspired Pso and Wavelet Neural Network

Zhang

2014

MCA

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Abstract-Network traffic flow prediction model is fundamental to the network performance evaluation and the design of network control scheme which is crucial for the success of high-speed networks. Aiming at shortcoming of the conventional network traffic time series prediction model and the problem that BP training algorithms easily plunge into local solution, a network traffic prediction model based on wavelet neural network and PSO-QI is presented in the paper. Firstly, the quantum principle obtained from Quantum PSO(QPSO)has been combined with standard PSO to form a new hybrid algorithm called PSO with Quantum Infusion(PSO-QI). Then, the parameters of wavelet neural network were optimized with PSO-QI and the time series of network traffic data was modeled and predicted based on wavelet neural network and PSO-QI. Experiments showed that PSOQI-wavelet neural network has better precision and adaptability compared with the traditional neural network.

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Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Cited by 69 publications

References 39 publications

A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number

A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number

A Learning Framework to Improve Video QoE in HTTP Adaptive Streaming SDN for Delivering Live Content

A Network Traffic Prediction Model Based on Quantum Inspired Pso and Wavelet Neural Network

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