Abstract
Network monitoring and analysis of consumption behavior are important aspects for network operators. The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over The Top applications are known by their large consumption of network resources. Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences. With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning. However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time. In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users’ Over The Top consumption behavior. Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way. After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.
Highlights
Over-the-top (OTT) media and communications services and applications are shifting the Internet consumption by increasing the traffic generation over the different available networks
Network monitoring and analysis of consumption behavior represents an important aspect for network operators since it allows to obtain vital information about consumption trends in order to offer new data plans aimed at specific users, obtain an adequate vision perspective of the information exchanged inside the network [5], detect potential threats, maintain the quality of the service, and prevent the collapse of networks, among other functionalities
It is important to mention that, for the time being, with the results observed on this first scenario it can be concluded that, while using the same dataset for training and testing, there is no major difference in terms of performance between some algorithms from both approaches
Summary
Over-the-top (OTT) media and communications services and applications are shifting the Internet consumption by increasing the traffic generation over the different available networks. Network monitoring and analysis of consumption behavior represents an important aspect for network operators since it allows to obtain vital information about consumption trends in order to offer new data plans aimed at specific users, obtain an adequate vision perspective of the information exchanged inside the network [5], detect potential threats, maintain the quality of the service, and prevent the collapse of networks, among other functionalities With this in mind, a proposal of personalizing service degradation policies applied to users once their data plan consumption limit is exceeded has been considered by implementing data mining methodologies and traditional machine learning algorithms [6].
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