Understanding the relationship between network traffic correlation and queueing behavior: A review based on the N -Burst ON/OFF model

Schwefel, Hans Peter; Antonios, Imad; Lipsky, Lester

doi:10.1016/j.peva.2017.07.002

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…Since the discovery of the self-similarity properties of network traffic in 1994 [14], various self-similarity-based traffic forecasting models have been proposed. One class of models describe the observed traffic by constructing physical models, including ON/OFF models with heavy-tailed distribution [15], M/G/∞ queuing models [7], etc. Another category is statistics-based models, which attempt to simulate network traffic through data fitting.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

Han,

Xiong,

2024

Aerospace

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Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency.

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Section: Methodsmentioning

confidence: 99%

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

Han,

Xiong,

2024

Aerospace

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“…The strictly alternating ON and OFF cycles are used to describe the alternation of the data sources of the satellite network nodes between sending data and nonsending data states [13]. Network traffic with self-similar characteristics is generated through the superposition of multiple independent ON/OFF service sources, and the heavy-tailed distribution of the ON cycle and OFF cycle of the ON/OFF service sources can be expressed using the Pareto distribution.…”

Section: Traffic Model Of Satellite Networkmentioning

confidence: 99%

Queue Management Algorithm for Satellite Networks Based on Traffic Prediction

2022

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In connexion with the effect of the self-similar characteristic of satellite network service traffic on queueing performance, a prediction model with optimised triple exponential smoothing is first established in this paper. This model performs network traffic prediction based on the dynamic triple exponential smoothing model and optimises the smoothing coefficient of the model through the differential evolution algorithm; a cubic function based on traffic prediction is further proposed to improve the adaptive random early detection (ARED) queue management algorithm. Based on the network traffic prediction results and the ARED, this algorithm uses the cubic function to perform nonlinear processing on the packet drop probability function. The simulation results show that the prediction model with optimised triple exponential smoothing has a high prediction accuracy, and the improved ARED algorithm based on the cubic function of traffic prediction in the presence of data bursts in self-similar traffic. It can effectively reduce the packet loss rate and improve the throughput, so as to better control the network congestion caused by self-similar traffic in satellite network.INDEX TERMS Dynamic triple exponential smoothing model, differential evolution algorithm, cubic function, adaptive random early detection.

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“…To capture autocorrelation of data over time, Markov modulated models were widely used. Even time series with long-range dependence, which are are common in many domains, can be successfully modeled by Markov models, see [ 19 , 20 ] for examples. The employment of a stochastic model, in addition to providing insights on time alignment error behavior under different setting, allows for a practical deployment without high computational burdens on measurement devices.…”

Section: Related Workmentioning

confidence: 99%

On the Calculation of Time Alignment Errors in Data Management Platforms for Distribution Grid Data

Schwefel

Antonios

Lipsky

2021

Sensors

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The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low- and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices.

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Understanding the relationship between network traffic correlation and queueing behavior: A review based on the N -Burst ON/OFF model

Cited by 11 publications

References 18 publications

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

Queue Management Algorithm for Satellite Networks Based on Traffic Prediction

On the Calculation of Time Alignment Errors in Data Management Platforms for Distribution Grid Data

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