Consumption Behavior Analysis of Over the Top Services: Incremental Learning or Traditional Methods?

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.

[1]  Matt Welsh,et al.  Flywheel: Google's Data Compression Proxy for the Mobile Web , 2015, NSDI.

[2]  Dinil Mon Divakaran,et al.  SLIC: Self-Learning Intelligent Classifier for network traffic , 2015, Comput. Networks.

[3]  Nick Feamster,et al.  uCap: An Internet Data Management Tool For The Home , 2015, CHI.

[4]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[5]  Muhammad N. Marsono,et al.  Online network traffic classification with incremental learning , 2016, Evol. Syst..

[6]  Albert Bifet,et al.  Efficient Online Evaluation of Big Data Stream Classifiers , 2015, KDD.

[7]  Anupam Joshi,et al.  Identification in Encrypted Wireless Networks Using Supervised Learning , 2014, 2014 IEEE Military Communications Conference.

[8]  Prasant Mohapatra,et al.  Efficient data capturing for network forensics in cognitive radio networks , 2011, ICNP 2011.

[9]  Nishanth R. Sastry,et al.  On factors affecting the usage and adoption of a nation-wide TV streaming service , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[10]  Juan Carlos Corrales,et al.  Personalized Service Degradation Policies on OTT Applications Based on the Consumption Behavior of Users , 2018, ICCSA.

[11]  Yong Wang,et al.  A Semi-Supervised Network Traffic Classification Method Based on Incremental Learning , 2013 .

[12]  Barbara Hammer,et al.  Incremental learning algorithms and applications , 2016, ESANN.

[13]  Juan Carlos Corrales,et al.  A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal , 2015, J. Comput..

[14]  Valentin Daniel Carela Español,et al.  Network traffic classification : from theory to practice , 2014 .

[15]  Richard Banks,et al.  You're capped: understanding the effects of bandwidth caps on broadband use in the home , 2012, CHI.

[16]  Fang Liu,et al.  Characterizing User Behavior in Mobile Internet , 2015, IEEE Transactions on Emerging Topics in Computing.

[17]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[18]  Guanglu Sun,et al.  Internet Traffic Classification Based on Incremental Support Vector Machines , 2018, Mob. Networks Appl..

[19]  Parag Kulkarni,et al.  An efficient approach for network traffic classification , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[20]  Daniel L. Silver,et al.  Machine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence , 2011, AGI.

[21]  Guanglu Sun,et al.  Traffic Classification Based on Incremental Learning Method , 2017 .

[22]  W. W. Daniel,et al.  Applied Nonparametric Statistics , 1978 .

[24]  Jean C. Walrand,et al.  Knowledge-Defined Networking: Modelització de la xarxa a través de l’aprenentatge automàtic i la inferència , 2016 .