On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Computer Networks and ISDN Systems
Two tools for network traffic analysis
Computer Networks: The International Journal of Computer and Telecommunications Networking
SNMP,SNMPV2,Snmpv3,and RMON 1 and 2
SNMP,SNMPV2,Snmpv3,and RMON 1 and 2
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Local dependencies and poissonification: a case study
Performance Evaluation
Predictive routing to enhance QoS for stream-based flows sharing excess bandwidth
Computer Networks: The International Journal of Computer and Telecommunications Networking - Small and home networks
On TCP and self-similar traffic
Performance Evaluation - Long range dependence and heavy tail distributions
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This paper describes a method to derive an appropriate prediction model for network traffic and verify its trustfulness. The proposal is not only an analysis of network packets but also finding a prediction method for the number of packets. We use time series prediction models and evaluate whether the model can predict network traffic exactly or not. In order to predict network packets in a certain time, the AR, MA, ARMA, and ARIMA model are applied. Our purpose is to find the most suitable model which can express the nature of future traffic among these models. We evaluate whether the models satisfy the stationary assumption for network traffic. The stationary assumption is obtained by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function) using a suitable significance. As the result, when network traffic is classified on a daily basis, the AR model is a good method to predict network packets exactly. The proposed prediction method can be used on a routing protocol as a decision factor for managing traffic data dynamically in a network.