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.

中文翻译：

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智能用户消费分析：基于增量学习的OTT服务降级

数据上限和服务降级是用于控制订户数据消耗的技术。这些技术的出现主要是由于连接用户数量及其功能丰富、带宽繁重的 Over-the-Top (OTT) 应用程序的数量不断增加而对网络堆栈的需求不断增长。在移动网络的范围内，传统运营商提供资源有限的用户数据计划，服务降级是用于限制消费的标准机制。限制用户数据使用有助于更好地利用资源并确保网络的可靠性能。然而，这种降级是以一种普遍的方式应用的，在不考虑行为的情况下影响所有用户应用程序。在本文中，我们提出了一个旨在解决这一限制的参考模型。具体来说，我们试图通过提供基于增量学习 (IL) 的用户 OTT 消费行为分类指南来个性化服务降级策略。我们通过调查几种 IL 算法对包含真实世界用户的 OTT 应用程序消费行为的数据集的功效，在案例研究中评估了我们模型的可行性。这些算法包括朴素贝叶斯 (NB)、K-最近邻 (KNN)、自适应随机森林 (ARF)、杠杆装袋 (LB)、Oza 装袋 (OB)、Learn++ 和多层感知器 (MLP)。获得的结果表明，ARF 以及 LB 和 ARF 之间的组合实现了最佳性能，分类精度和召回率均超过 90%。基于获得的结果，我们提出了服务降级策略以支持关键任务系统的决策。我们认为我们的模型在实际场景中具有很强的适用性，尤其是在用户消费分析中。我们的参考模型为在当前和未来网络（如 5G）中定义个性化服务降级策略时需要执行的任务提供了概念基础。据我们所知，这项工作是这方面的第一项工作。