Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation
Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-he...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9258898/ |
| _version_ | 1818853482406346752 |
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| author | Juan Sebastian Rojas Adrian Pekar Alvaro Rendon Juan Carlos Corrales |
| author_facet | Juan Sebastian Rojas Adrian Pekar Alvaro Rendon Juan Carlos Corrales |
| author_sort | Juan Sebastian Rojas |
| collection | DOAJ |
| description | Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing real-world users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in mission-critical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter. |
| first_indexed | 2024-12-19T07:37:31Z |
| format | Article |
| id | doaj.art-c9392f5e63b04f3bb905221601fd9a0a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:37:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c9392f5e63b04f3bb905221601fd9a0a2022-12-21T20:30:32ZengIEEEIEEE Access2169-35362020-01-01820742620744210.1109/ACCESS.2020.30379719258898Smart User Consumption Profiling: Incremental Learning-Based OTT Service DegradationJuan Sebastian Rojas0https://orcid.org/0000-0001-8356-5805Adrian Pekar1https://orcid.org/0000-0003-4511-8267Alvaro Rendon2https://orcid.org/0000-0002-2935-7316Juan Carlos Corrales3https://orcid.org/0000-0002-5608-9097Telematics Engineering Research Group, Universidad del Cauca, Popayán, ColombiaDepartment of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, HungaryTelematics Engineering Research Group, Universidad del Cauca, Popayán, ColombiaTelematics Engineering Research Group, Universidad del Cauca, Popayán, ColombiaData caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing real-world users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in mission-critical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter.https://ieeexplore.ieee.org/document/9258898/Over-the-top applicationclassificationincremental learningservice degradationdecision making |
| spellingShingle | Juan Sebastian Rojas Adrian Pekar Alvaro Rendon Juan Carlos Corrales Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation IEEE Access Over-the-top application classification incremental learning service degradation decision making |
| title | Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation |
| title_full | Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation |
| title_fullStr | Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation |
| title_full_unstemmed | Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation |
| title_short | Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation |
| title_sort | smart user consumption profiling incremental learning based ott service degradation |
| topic | Over-the-top application classification incremental learning service degradation decision making |
| url | https://ieeexplore.ieee.org/document/9258898/ |
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