Management of internet bandwidth using machine learning technique

In universities, the amount of Internet traffic fluctuates over the time of a day and over the period of a semester. With the limited bandwidth resource available and ever increasing demand for high throughput due to multi-media, the Internet bandwidth have to be managed within an organization so that the priority traffic critical to the business is not slowed down by less critical, often entertainment, traffic. An important aspect in setting the policy of the bandwidth management is the knowledge of the Internet usage patterns over a period of time. The ability to predict the usage patterns provides the policy maker a powerful tool to set the rules and policies such that the allocation of Internet bandwidth is conducted dynamically in favor of business critical traffic while at the same time aiming to serve all users subject to the availability of the total available bandwidth. This paper reports the initial experiment of predicting the Internet usage patterns using RapidMiner Machine Learning tools with SVM algorithm for the IT Service Department of Sanata Dharma University in Indonesia. The algorithm used the dataset captured from the university over a period of time as the basis for prediction. The result of the dataset analysis is intended to the basis of policies development relating to bandwidth management