Optimising Self-Similarity Network Traffic for Better Performance
| Autor: | N V Blamah, V K Abanihi, K T Anyachebelu, A B Ikharo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Self-similarity
business.industry Computer science 020209 energy 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence business Machine learning computer.software_genre computer |
| Zdroj: | International Journal of Scientific Research in Science and Technology. :164-176 |
| ISSN: | 2395-602X 2395-6011 |
| DOI: | 10.32628/ijsrst207413 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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