BitTorrent Network Traffic Forecasting With ARMA

Hoong, Poo Kuan

doi:10.5121/ijcnc.2012.4409

Cited by 31 publications

(8 citation statements)

References 14 publications

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“…Poor planning can lead to a high degree of concatenation between the configuration of an individual data stream and the configuration of global (network-wide) traffic [3]. Therefore, forecasting network traffic is an important task [4]. Operators of cellular networks plan network traffic especially carefully in order to ensure network performance and quality of service (QoS) [5].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Дослідження Завантаження Інформаційної Мережі В Умовах Дистанційної Освіти І Дистанційного Моніторингу

2023

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The method of estimating the bandwidth of communication channels and routing of Internet packets in the conditions of distance learning is considered. The research of media service platforms Microsoft Teams, Google Zoom and Skype was performed. A feature of the method is a combination of direct and indirect methods of estimating the bandwidth of communication channels. The application of this methodology makes it possible to evaluate existing market solutions for the implementation of distance education. This allows us to conclude on the feasibility of using the above services for distance learning. As a result, such a study helps to assess the congestion of channels and the effectiveness of remote monitoring of remote objects when the time delay in the delivery of information should not exceed a certain value. The results of the experiment showed that in general the considered platforms are very similar in the mode of conversation using only voice traffic. Bitrate in this case ranges from 100-130 Kbit/s. There is a significant difference between the platforms during the video traffic session. The differences between the platforms in the bitrate for this case are more than 90% (in some cases more than 100%). The study showed that the workload of the Internet channel on weekdays increased after ten o'clock in the afternoon. The Microsoft Teams platform provides the best video image quality. There are no differences between the platforms in terms of sound quality. General recommendations for organizing an online TV broadcast are as follows: for voice traffic, you can choose any of the proposed platforms, due to the fact that the difference in their characteristics is insignificant it's better to use Google Zoom to organize video communication; it's best to choose the Microsoft Teams media platform to organize a screen demonstration session. Studies have made it possible to assess the load that occurs during a communication session. The results of the experiment showed that the Google Zoom media platform, with the same quality of communication, creates fewer loads on the main Internet channels. The technical result obtained in this work is the creation of tools for selecting the optimal media platform for remote communication.

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Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Дослідження Завантаження Інформаційної Мережі В Умовах Дистанційної Освіти І Дистанційного Моніторингу

2023

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“…The modelling network traffic is very important in large scale networks for planning and it increases the performance of the network. In [31], [32][33] the ARMA model to predict network traffic is proposed. [34][35] present the ARIMA model to analyse and predict future traffic in the network.…”

Section: Background Of the Studymentioning

confidence: 99%

Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

et al. 2020

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Network traffic analysis and predictions have become vital for monitoring networks. Network prediction is the process of capturing network traffic and examining it deeply to decide what is the occurrence in the network. The accuracy of analysis and estimation of network traffic are increasingly becoming significant in achieving guaranteed Quality of Service (QoS) in the network. The main aim of the presented research is to propose a new methodology to improve network traffic prediction by using sequence mining. The significance of this important topic lies in the urge to contribute to solving the research problem in network traffic prediction intelligently. We propose an integrated model that combines clustering with existing series models to enhance prediction the network traffic. Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series. The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Furthermore, we have suggested using the weighted exponential smoothing model as preprocessing stages for increasing the reliability of the proposed model. In this research paper, machine intelligence proposed to predict network traffic. The machine intelligence is working as pre-processing for enhancing the existing time series models. The machine intelligence combines non-crisp Fuzzy-C-Means (FCM) clustering and the weight exponential method for improving deep learning Long Short-Time Memory(LSTM)and Adaptive Neuro-Fuzzy Inference System (ANFIS)time series models. The ANFIS and LSMT time series models are applied to predict network traffic. Two real network traffic traces were conducted to test the proposed time series models. The empirical results of proposed to enhanced LSTM 97.95% and enhanced ANFIS model is R=96.78% for cellular traffic data, with respect to the correlation indicator. It is observed that the proposed model outperforms alternative time series models. A comparative prediction results between the proposed model and exis ting time series models are presented. The comparisons indicate that the presented model outperforms the opponent models; the proposed method optimises the deep learning LSTM and ANFIS time series models. The proposed methodology offers more effective approach to the prediction of network traffic.

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“…Ng Kar et al [10] introduced ARMA time series model for predicting large file transfer of network traffic. Authors experimented with File Transfer Protocol (FTP) data set.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Single Moving Average Time Series Model Using Rough k-Means for Prediction of Network Traffic.

Aldhyani¹,

Joshi²

2017

IJERA

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In the last decade, real-time audio and video services have gained much popularity, and now occupying a large portion of the total network traffic in the Internet. As the real-time services are becoming mainstream the demand for Quality of Service (QoS) is greater than ever before. It is necessary to use the network resources to the fullest, to satisfy the increasing demand for QoS. To solve this issue, we need to apply a prediction model for network traffic, on the basis of network management such as congestion control and bandwidth location. In this paper, we propose an integrated model that combines Rough K-Means (RKM) clustering with Single Moving Average (SMA) time series model to improve prediction loading packets of network traffic. The single moving average time series prediction model is used to predict loading of packets volume in real network traffic. Further, clustering granules obtained by using rough k-means is used to analyze the network data of each year separately. The proposed model is an integration of the prediction results that were obtained from conventional single moving average prediction model with centriods of clusters that obtained from rough k-means clustering. The model is evaluated using on line network traffic data that has been collected from WIDE backbone Network MSE, RMSE and MAPE metrics are used to examine the results of the integrated model. The experimental results show that the integrated model can be an effective way to improve prediction accuracy achieved with the help of rough k-means clustering. A Comparative result between conventional prediction model and our integrated model is presented.

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BitTorrent Network Traffic Forecasting With ARMA

Cited by 31 publications

References 14 publications

Дослідження Завантаження Інформаційної Мережі В Умовах Дистанційної Освіти І Дистанційного Моніторингу

Дослідження Завантаження Інформаційної Мережі В Умовах Дистанційної Освіти І Дистанційного Моніторингу

Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

Enhancement of Single Moving Average Time Series Model Using Rough k-Means for Prediction of Network Traffic.

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