A Prediction Method of Network Traffic Using Time Series Models

Jung, Souhwan; Kim, Chonggun; Chung, Youngjin

doi:10.1007/11751595_26

Cited by 16 publications

(17 citation statements)

References 6 publications

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“…Jung et al [12] used AR model to predict network congestion, followed by another method [33] which focuses on controlling the traffic using routing techniques. Long-range dependence and self-similarity in larger time span are the characteristics which should be captured by a good traffic model [36] .…”

Section: Congestion Control Using Predictive Analyticsmentioning

confidence: 99%

Prediction and preemptive control of network congestion in distributed real-time environment

Dhanoa

Haque

2016

2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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Due to ever increasing demand for network capacity, the congestion problem is inflating. Congestion results in queuing within the network, packet loss and increased delays. It should be controlled to increase the system throughput and quality of service. The existing congestion control approaches such as source throttling and rerouting focus on controlling congestion after it has already happened. However, it is much more desirable to predict future congestion based on the current state and historical data, so that efficient controlling techniques can be applied to prevent congestion from happening in future. We have proposed a Neural Network Prediction-based routing (NNPR) protocol to predict as well as control the network traffic in distributed real time environment. A distributed real time transaction processing simulator (DRTTPS) has been used as the test-bed. For predictions, multi-step neural network model is developed in SPSS Modeler, which predicts congestion in future. ADAPA (Adaptive Decision and Predictive Analytics) scoring engine has been used for real-time scoring. An ADAPA wrapper calls the prediction model through web services and predicts the congestion in real-time. Once predicted results are obtained, messages are rerouted to prevent congestion. To compare our proposed work with existing techniques, two routing protocols are also implemented-Dijkstra's Shortest Path (DSP) and Routing Information Protocol (RIP). The main metric used to analyze the performance of our protocol is the percentage of transactions which complete before their deadline. The NNPR protocol is analyzed with various simulation runs having parameters both inside and outside the neural network input training range. Various parameters which can cause congestion were studied. These include bandwidth, worksize, latency, max active transactions,

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Section: Congestion Control Using Predictive Analyticsmentioning

confidence: 99%

Prediction and preemptive control of network congestion in distributed real-time environment

Dhanoa

Haque

2016

2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

View full text Add to dashboard Cite

show abstract

“…Jung et al [12] used AR model to predict network congestion, followed by another method [33] which focuses on controlling the traffic using routing techniques.…”

Section: Time Series Predictionmentioning

confidence: 99%

“…The stationary assumption can be measured by ACF and PACF. To predict the network packets, traffic monitoring was done for one year by connecting to intra-network [12]. The total number of packets was measured every hour.…”

Section: Time Series Predictionmentioning

confidence: 99%

“…MSE indicates the difference between predicted and estimated values. Some models for time series prediction are described in [12] and summarized below:…”

Section: Time Series Predictionmentioning

confidence: 99%

“…There are many techniques to control congestion [11], but these techniques control the congestion after it has happened. With an increased demand for highly efficient networks, it is important to analyze the potential network traffic beforehand and predict the traffic [12], so that efficient control techniques can be applied.…”

Section: Introductionmentioning

confidence: 99%

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