Prediction of traffic flow in cloud computing at a service provider.
| dc.contributor.author | Sekwatlakwatla, Prince | |
| dc.contributor.co-supervisor | Kwuimi, Raoul | |
| dc.contributor.supervisor | Zuva, Tranos, Prof | |
| dc.date.accessioned | 2022-01-27T02:41:45Z | |
| dc.date.available | 2022-01-27T02:41:45Z | |
| dc.date.issued | 2018-11 | |
| dc.description | M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences) Vaal University of Technology. | en_US |
| dc.description.abstract | Cloud computing provides improved and simplified IT management and maintenance capabilities through central administration of resources. Companies of all shapes and sizes are adapting to this new technology. Although cloud computing is an attractive concept to the business community, it still has some challenges such as traffic management and traffic prediction that need to be addressed. Most cloud service providers experience traffic congestion. In the absence of effective tools for cloud computing traffic prediction, the allocation of resources to clients will be ineffective thus driving away cloud computing users. This research intends to mitigate the effect of traffic congestion on provision of cloud service by proposing a proactive traffic prediction model that would play an effective role in congestion control and estimation of accurate future resource demand. This will enhance the accuracy of traffic flow prediction in cloud computing by service providers. This research will evaluate to determine the performance between Auto-regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) as prediction tools for cloud computing traffic. These two techniques were tested by using simulation to predict traffic flow per month and per year. The dataset was downloaded data taken from CAIDA database. The two algorithms Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) where implemented and tested separately. Experimental results were generated and analyzed to test the effectiveness of the traffic prediction algorithms. Finally, the findings indicated that ARIMA can have 98 % accurate prediction results while ANN produced 89 % accurate prediction results. It was also observed that both models perform better on monthly data as compared to yearly data. This study recommends ARIMA algorithm for data flow prediction in private cloud computing | en_US |
| dc.identifier.uri | http://hdl.handle.net/10352/472 | |
| dc.language.iso | en | en_US |
| dc.subject | Cloud computing, traffic, traffic prediction, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN) | en_US |
| dc.subject.lcsh | Dissertations, Academic. | en_US |
| dc.subject.lcsh | Cloud computing. | en_US |
| dc.title | Prediction of traffic flow in cloud computing at a service provider. | en_US |
| dc.type | Thesis | en_US |