Design, implementation, and evaluation of learning algorithms for dynamic real-time network monitoring,International Journal of Network Management

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Design, implementation, and evaluation of learning algorithms for dynamic real-time network monitoring
International Journal of Network Management ( IF 1.5 ) Pub Date : 2020-03-30 , DOI: 10.1002/nem.2108
Rashid Mijumbi ₁ , Abhaya Asthana ₂ , Markku Koivunen ₃ , Fu Haiyong ₄ , Qinjun Zhu ₄

Affiliation

Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state-of-the-art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning-based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms.

中文翻译：

动态实时网络监控学习算法的设计、实现和评估

为了保证电信网络的高可靠性和可用性，网络监控是必要的。最先进的监控工具带来的主要挑战之一是创建网络基线。此类基线包括可用于确定监控值（具有给定上下文，例如时间）是否代表正常网络操作的阈值。当前（和未来）网络的规模和复杂性使得手动确定和设置每个网络运营商和指标的基线变得不可行，更不用说根据网络条件的变化调整阈值了。这导致使用默认基线和/或只设置一次基线并且在网络元素的整个生命周期中永远不会改变它们。这不仅会导致运行效率低下，还会对网络可靠性和可用性产生影响。在本文中，我们介绍了 DARN 的设计、实现和评估：一组基于分析和机器学习的算法，旨在确保网络基线自动适应不同的度量演变。DARN 已经在具有真实流量的部署上进行了全面评估，以确认生成的基线的准确性，由于基线适应而使准确性提高了 22%，误报减少了 72%。

更新日期：2020-03-30

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