2016
DOI: 10.1016/j.comnet.2016.08.027
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Predicting expected TCP throughput using genetic algorithm

Abstract: Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of ma… Show more

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Cited by 12 publications
(3 citation statements)
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“…Similarly, Yoo and Sim [9] predict the expected bandwidth utilization on high-bandwidth wide area networks using autoregressive integrated moving average (ARIMA). However, ARMA, DHR, and ARIMA rely heavily on user experience and give rise to a regression model that cannot be directly applied to data traffic with unknown characteristics [10]. Machine learning methods such as neural networks (NNs) [11] and support vector regression (SVR) [12], on the other hand, allow the development of a regression model without prior knowledge of the characteristic of the traffic data.…”
Section: Related Workmentioning
confidence: 99%
“…Similarly, Yoo and Sim [9] predict the expected bandwidth utilization on high-bandwidth wide area networks using autoregressive integrated moving average (ARIMA). However, ARMA, DHR, and ARIMA rely heavily on user experience and give rise to a regression model that cannot be directly applied to data traffic with unknown characteristics [10]. Machine learning methods such as neural networks (NNs) [11] and support vector regression (SVR) [12], on the other hand, allow the development of a regression model without prior knowledge of the characteristic of the traffic data.…”
Section: Related Workmentioning
confidence: 99%
“…The authors also concluded that a prediction model in reality is decided by the different characteristics of the specific data and a specific model cannot always be the optimal model for all. Although the conventional forecasting models have been successfully applied to prediction, these models have somewhat several limitations in their ability to make predictions in certain situations (Hernandez Benet et al, 2016;Park and Lee, 1995). In recent years, intelligence computational techniques including machine learning techniques, wavelet transform and fuzzy methods have been proposed to form analysis method for prediction.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, a time series model can be combined with a genetic algorithm in forecasting [32]. Indeed, a genetic algorithm has been used to optimize values produced by a grey model.…”
Section: Genetic Algorithmsmentioning
confidence: 99%