Characterizing and Modeling WAN Egress Traffic
| dc.contributor.advisor | Yalemzewd, Negash (PhD) | |
| dc.contributor.author | Rahel, Abera | |
| dc.date.accessioned | 2018-09-24T04:45:10Z | |
| dc.date.accessioned | 2023-11-04T15:14:38Z | |
| dc.date.available | 2018-09-24T04:45:10Z | |
| dc.date.available | 2023-11-04T15:14:38Z | |
| dc.date.issued | 2018-05 | |
| dc.description.abstract | Computer networks exhibit complex characteristics due to the heterogenous nature of traffic running through the network. This makes the design of reliable networks and network services difficult. To have a design of robust and reliable networks, a detailed understanding of traffic characteristics of the network is needed which will lead to distinguish the traffic model it fits. In this paper, it is showed that the WAN egress traffic possess self-similar characteristics, using different mathematical techniques. And also, the presence of long memory in WAN egress traffic is shown by the Autocorrelation Function of the trace. Additionally, it is showed that one of the self-similar long memory models, Fractional Auto-Regressive Integrated Moving Average (FARIMA) model, best capture the collected WAN traffic data. To model the traffic data first stationarity was tested using Augmented Dickey Fuller (ADF) test. The AR and MA terms of the model are estimated using the ACF and PACF plot. To test the model, Autocorrelation function is used, and it is found that the Autocorrelation function of the approximated data has a resemblance to the Autocorrelation function of the collected data. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/12134 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAU | en_US |
| dc.subject | WAN | en_US |
| dc.subject | Self-similarity | en_US |
| dc.subject | Long Range Dependence | en_US |
| dc.subject | Traffic models | en_US |
| dc.subject | FARIMA | en_US |
| dc.title | Characterizing and Modeling WAN Egress Traffic | en_US |
| dc.type | Thesis | en_US |