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Deep-FDA: Using Functional Data Analysis and Neural Networks to Characterize Network Services Time Series
IEEE Transactions on Network and Service Management ( IF 5.3 ) Pub Date : 2021-01-25 , DOI: 10.1109/tnsm.2021.3053835
Daniel Perdices , Jorge E. Lopez de Vergara , Javier Ramos

In network management, it is important to model baselines, trends, and regular behaviors to adequately deliver network services. However, their characterization is complex, so network operation and system alarming become a challenge. Several problems exist: Gaussian assumptions cannot be made, time series have different trends, and it is difficult to reduce their dimensionality. To overcome this situation, we propose Deep-FDA, a novel approach for network service modeling that combines functional data analysis (FDA) and neural networks. Specifically, we explore the use of functional clustering and functional depth measurements to characterize network services with time series generated from enriched flow records, showing how this method can detect different separated trends. Moreover, we augment this statistical approach with the use of autoencoder neural networks, improving the classification results. To evaluate and check the applicability of our proposal, we performed experiments with synthetic and real-world data, where we show graphically and numerically the performance of our method compared to other state-of-the-art alternatives. We also exemplify its application in different network management use cases. The results show that FDA and neural networks are complementary, as they can help each other to improve the drawbacks that both analysis methods have when are applied separately.

中文翻译:

FDA深入研究:使用功能数据分析和神经网络来表征网络服务时间序列

在网络管理中,为基线,趋势和常规行为建模以充分交付网络服务很重要。但是,它们的特征很复杂,因此网络操作和系统警报成为一个挑战。存在几个问题:无法做出高斯假设,时间序列具有不同的趋势,并且很难减小其维数。为了克服这种情况,我们提出了Deep-FDA,这是一种结合功能数据分析(FDA)和神经网络的网络服务建模新方法。具体来说,我们探索使用功能聚类和功能深度测量来表征网络服务的特征,这些服务具有从丰富的流量记录中生成的时间序列,从而说明该方法如何检测不同的分离趋势。而且,我们使用自动编码器神经网络扩充了这种统计方法,从而改善了分类结果。为了评估和验证我们的建议的适用性,我们使用合成数据和真实数据进行了实验,与其他最新技术相比,我们以图形和数字方式显示了我们方法的性能。我们还将在不同的网络管理用例中举例说明其应用。结果表明,FDA和神经网络是互补的,因为它们可以互相帮助,以改善两种分析方法分别应用时所具有的缺点。与其他最新技术相比,我们以图形和数字方式显示了我们方法的性能。我们还将在不同的网络管理用例中举例说明其应用。结果表明,FDA和神经网络是互补的,因为它们可以互相帮助,以改善两种分析方法分别应用时所具有的缺点。与其他最新技术相比,我们以图形和数字方式显示了我们方法的性能。我们还将在不同的网络管理用例中举例说明其应用。结果表明,FDA和神经网络是互补的,因为它们可以互相帮助,以改善两种分析方法分别应用时所具有的缺点。
更新日期:2021-03-12
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