Internet Traffic Forecasting using Neural Networks

Cortez, Paulo; Rio, Miguel; Rocha, Miguel; Sousa, Pedro

doi:10.1109/ijcnn.2006.247142

Cited by 49 publications

(23 citation statements)

References 19 publications

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“…They observed that their mode is giving prediction efficiency and the accuracy. Daniel et al [8] proposed Simple Moving Average (SMA) and Exponential Moving Average (EMA) for prediction network traffic. They collected real network traffic from web services to test their model.…”

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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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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“…A time series is a sequence of data points or time ordered observations (y 1 , y 2 , ..., y t ) in which each period recorded at a specific time t. A time series forecasting is the use of a model to predict future values based on previously observed values [29,46]. For time series feature extraction, a trace (set of events) should be converted into time series with the regular time intervals.…”

Section: Time Series Analysismentioning

confidence: 99%

“…The choice of the input time intervals has a crucial effect in the PIT entries forecasting performance. A small number of time intervals will provide insufficient information, while a high number of intervals will increase the probability of irrelevant input features [29]. Several configurations based on our observations of PIT entries fluctuation in considered network topologies were set.…”

Section: Phase 1: Adaptive Trainingmentioning

confidence: 99%

“…In particular, the field of Time Series Forecasting (TSF) deals with the prediction of a chronologically ordered values [27,28]. The goal of TSF is to model a complex system as a black-box in order to predict the systems behavior based on the historical data [21,29]. In this paper, we develop ACCPndn (Adaptive Congestion Control Protocol in Named Data Networking) which is a new congestion control protocol with learning capacities.…”

Section: Introductionmentioning

confidence: 99%

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ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Karami

2015

Journal of Network and Computer Applications

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase -adaptive trainingwe propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance-we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and highutilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

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“…One addresses the forecasting of hourly traffic of the next day based on past observations, and the other focuses on the peak load prediction of the next day. Based on Time Series Forecasting (TSF), (Cortez et al, 2006) uses a Neural Network Ensemble (NNE) to predict the TCP/IP traffic. Neural network based traffic prediction approach is complicated to implement.…”

Section: Application Of Neural Network In Traffic Forecastingmentioning

confidence: 99%