A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application

Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolvi...

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Autores principales:	Nikita Jalodia, Mohit Taneja, Alan Davy
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Network function virtualization machine learning deep learning neural networks classification algorithms multi-label classification Telecommunication TK5101-6720 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/83f6a8b3ab3e49cf91ea2c84b8358bfe
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spelling	oai:doaj.org-article:83f6a8b3ab3e49cf91ea2c84b8358bfe2021-11-18T00:11:29ZA Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application2644-125X10.1109/OJCOMS.2021.3122844https://doaj.org/article/83f6a8b3ab3e49cf91ea2c84b8358bfe2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592636/https://doaj.org/toc/2644-125XRecent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.Nikita JalodiaMohit TanejaAlan DavyIEEEarticleNetwork function virtualizationmachine learningdeep learningneural networksclassification algorithmsmulti-label classificationTelecommunicationTK5101-6720Transportation and communicationsHE1-9990ENIEEE Open Journal of the Communications Society, Vol 2, Pp 2469-2493 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Network function virtualization machine learning deep learning neural networks classification algorithms multi-label classification Telecommunication TK5101-6720 Transportation and communications HE1-9990
spellingShingle	Network function virtualization machine learning deep learning neural networks classification algorithms multi-label classification Telecommunication TK5101-6720 Transportation and communications HE1-9990 Nikita Jalodia Mohit Taneja Alan Davy A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
description	Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.
format	article
author	Nikita Jalodia Mohit Taneja Alan Davy
author_facet	Nikita Jalodia Mohit Taneja Alan Davy
author_sort	Nikita Jalodia
title	A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
title_short	A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
title_full	A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
title_fullStr	A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
title_full_unstemmed	A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application
title_sort	deep neural network-based multi-label classifier for sla violation prediction in a latency sensitive nfv application
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/83f6a8b3ab3e49cf91ea2c84b8358bfe
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A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application

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