Analysis and modelling of traffic produced by adaptive HTTP-based video

Abstract

The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

Efficiency of Authored Mixed Prediction Model with Application to the Labor Market

“…In this part of the paper we present the results of the implementation of the proprietary wavelet model described in Chapter 1. For the implementation of the model the time series was chosen showing the number of unemployed people in countries in the Eurozone (the series included in the study covers the period 1997-2016((EA11-2000, EA12-2006, EA13 -2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, the annual average, 1000 people).). Data on the number of unemployed people in the Eurozone used to implement the model was taken from the Eurostat database (Figure 3).…”

“…Also interesting are algorithms that use ARIMA, ARMA. FARMA with artificial neural networks (see: Biernacki, 2016;Hadaś-Dyduch, 2017a) and stochastic models (Biernacki, 2007). Although stochastic models are based on Markov processes and therefore differ in complexity and approximation accuracy.…”

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

“…Therefore, Capacity and frequency optimization are complicated and time taking as the complexity of the cellular backhaul network is increasing [10], [11], [12]. Dynamic resource allocation becomes and essential technique for resource planning and deploy them optimally, it also helps to increase the primary and secondary network throughput [13]. Predicting the future is the key for optimal optimization and resource allocation, and forecasting has a proven success in different area of communication network for better network optimization and management [14].…”

Traffic prediction methods for quality improvement of adaptive video

“…In [35], we proposed to employ (F)ARIMA processes and ANNs to enhance performance of adaptive video services. However, the experiment were done on aggregated network traffic produced in an emulated environment.…”

“…In contrast to all the mentioned works but [8] and [35], we treat the capacity measurement as a long range-dependent process. Hence, for the capacity modelling, we use, among others, a fractal autoregressive model (FARIMA).…”