Deep Learning for Autonomic SLA Management of NFV Resources towards Next Generation Networks

Jalodia, Nikita (2022) Deep Learning for Autonomic SLA Management of NFV Resources towards Next Generation Networks. Doctoral thesis, SETU Waterford.

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Recent advancements in the domain of Network Function Virtualization (NFV), and the rollout of next-generation networks have led to a new era of applications delivered via a paradigm of flexible and softwarized communication networks. This has opened the market to a wider movement towards virtualized applications and services in key verticals such as automated vehicles, smart grid, virtual reality (VR), Internet of Things (IoT), industry 4.0, telecommunications, etc. This has 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, 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. The research presented in this work aims to address the challenges behind effectively managing the trade-off between efficiency and reliability when considering latency critical applications based in a high-availability network slice in next-generation softwarized networks. In the course of research done in this work, we design and develop algorithms to address the complexity towards meeting QoS demands in serving upcoming verticals through the softwarised network architecture, and develop deep learning based frameworks for proactive SLA management in the use-case of a latency-critical NFV application. 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 leverage various machine learning based methodologies to proactively 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 in future.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: SLA Management, Next Generation Networks
Departments or Groups: *NONE OF THESE*
Divisions: School of Science > Department of Computing, Maths and Physics
Depositing User: Derek Langford
Date Deposited: 01 Dec 2022 10:50
Last Modified: 01 Dec 2022 10:50

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