Deep-FDA: Using Functional Data Analysis and Neural Networks to Characterize Network Services Time Series

Perdices, Daniel; Vergara, Jorge E. López de; Ramos, Javier

doi:10.1109/tnsm.2021.3053835

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…[41], which complicates their analysis. In recent years, these systems have been experiencing constant shifts in volume and structure [42]. Additionally, the near-daily deployment of new services has significantly heightened the complexity of monitoring and alarming in such environments.…”

Section: Discussionmentioning

confidence: 99%

Interactivity Anomaly Detection in Remote Work Scenarios Using LSTM

Arellano-Uson,

Magaña,

Morató

et al. 2024

IEEE Access

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In recent years, there has been a notable surge in the utilization of remote desktop services, largely driven by the emergence of new remote work models introduced during the pandemic. These services cater to interactive cloud-based applications (CIAs), whose core functionality operates in the cloud, demanding strict end-user interactivity requirements. This boom has led to a significant increase in their deployment, accompanied by a corresponding increase in associated maintenance costs. Service administrators aim to guarantee a satisfactory Quality of Experience (QoE) by monitoring metrics like interactivity time, particularly in cloud environments where variables such as network performance and shared resources come into play. This paper analyses anomaly detection state of the art and proposes a novel system for detecting interactivity time anomalies in cloud-based remote desktop environments. We employ an automatic model based on LSTM neural networks that achieves an accuracy of up to 99.97%.INDEX TERMS remote work, interactivity time, anomaly detection, LSTM, cloud-based interactive applications, remote desktop, QoE.

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Section: Discussionmentioning

confidence: 99%

Interactivity Anomaly Detection in Remote Work Scenarios Using LSTM

Arellano-Uson,

Magaña,

Morató

et al. 2024

IEEE Access

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show abstract

“…11 to 19) (Simonyan and Zisserman, 2015). DNNs can deal with various model inputs such as images, texts, and also functional data (Perdices et al , 2021). The larger number of intermediate layers in DNNs provides the capacity to learn multiple levels of abstraction of the input data.…”

Section: Deep Learningmentioning

confidence: 99%

Machine learning approaches applied in spinal pain research

Falla

Devecchi

Jiménez-Grande

et al. 2021

Journal of Electromyography and Kinesiology

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“…More recently, considerable attention has been paid to integrating functional data into the procedures of machine learning, e. g. neural networks or deep learning (e.g. Perdices et al (2021) and Rao et. al.…”

Section: Introductionmentioning

confidence: 99%

Minimax estimation of the mode of functional data

Müller

2023

Journal of Multivariate Analysis

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Deep-FDA: Using Functional Data Analysis and Neural Networks to Characterize Network Services Time Series

Cited by 10 publications

References 37 publications

Interactivity Anomaly Detection in Remote Work Scenarios Using LSTM

Interactivity Anomaly Detection in Remote Work Scenarios Using LSTM

Machine learning approaches applied in spinal pain research

Minimax estimation of the mode of functional data

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