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

Jalodia, Nikita and Taneja, Mohit and Davy, Alan (2021) A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application. IEEE Open Journal of the Communications Society, 2. pp. 2469-2493. ISSN 2644-125X

Full text not available from this repository. (Request a copy)


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.

Item Type: Article
Additional Information: Publisher Copyright: © 2020 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
Departments or Groups:
Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:02
Last Modified: 14 Aug 2023 23:00
URI: http://repository-testing.wit.ie/id/eprint/3743

Actions (login required)

View Item View Item