Browsing by Author "Ikharo, A.B."

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    Neural Network Prediction of Self-Similarity Network Traffic
    (Department of Computer Science, Nasarawa State University Keffi, 2022-12-02) Ikharo, A.B.; Anyachebelu, Tochukwu Kene
    Several factors are found to influence either short or long-term burstiness in Transmission Control Protocol (TCP) flow across many networking facilities and services. Predicting such self-similar traffic has become necessary to achieve better performance. In this study, ANN model was deployed to simulate College Campus network traffic. A Feed Forward Backpropagation Artificial Neural Network (ANN) and Wireshark tools were implemented to study the network Scenario. The predicted series were then compared with the corresponding real traffic series (Mobile Telephone-Network (MTN)-Nigeria). Suitable performance measurements of the Means Square Error (MSE) and the Regression Coefficient were used. Our results showed that burstiness is present in the network across many time scales. With the increasing number of data packet distributions thereby providing a steady flow of burst over the entire period of system load as the traffic network performance improves.
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    Optimising Self-Similarity Network Traffic for Better Performance
    (Department of Computer Science, Nasarawa State University Keffi, 2020-08-08) Ikharo, A.B.; Anyachebelu, Tochukwu Kene; Blamah, N.V.; Abanihi, V.K.
    Given the ubiquity of the burstiness present across many networking facilities and services, predicting and managing self-similar traffic has become a key issue owing to new complexities associated with self-similarity which makes difficult the achievement of high network performance and quality of service (QoS). In this study ANN model was used to model and simulate FCE Okene computer network traffic. The ANN is a 2-39-1 Feed Forward Backpropagation network implemented to predict the bursty nature of network traffic. Wireshark tools that measure and capture packets of network traffic was deployed. Moreover, variance-time method is a log-log scale plot, representing variance versus a non-overlapping block of size m aggregate variance level engaged to established conformity of the ANN approach to self-similarity characteristic of the network traffic. The predicted series were then compared with the corresponding real traffic series. Suitable performance measurements used were the Means Square Error (MSE) and the Regression Coefficient. Our results showed that burstiness is present in the network across many time scales. The study also established the characteristic property of a long-range dependence (LRD). The work recommended that network traffic observation should be longer thereby enabling larger volume of traffic to be capture for better accuracy of traffic modelling and prediction.