A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case

TL;DR

This paper presents a proactive SLA management framework using Graph Neural Networks and Deep Reinforcement Learning to optimize latency-critical NFV applications in 5G/6G networks, improving efficiency and reliability.

Contribution

It introduces a graph-based spatio-temporal forecasting model and a DRL-based dynamic SLA oversight mechanism for NFV latency-critical services.

Findings

74.62% improved forecasting performance over baseline models

Effective dynamic SLA-aware scaling policy management

Enhanced balance between efficiency and reliability in NFV applications

Abstract

Recent advancements in the rollout of 5G and 6G have led to the emergence of a new range of latency-critical applications delivered via a Network Function Virtualization (NFV) enabled paradigm of flexible and softwarized communication networks. Evolving verticals like telecommunications, smart grid, virtual reality (VR), industry 4.0, automated vehicles, etc. are driven by the vision of low latency and high reliability, and there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for both the service providers and the end-user. In this work, we look to tackle the over-provisioning of latency-critical services by proposing a proactive SLA management framework leveraging Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to balance the trade-off between efficiency and reliability. To summarize our key contributions: 1) we compose a graph-based spatio-temporal multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, delivering 74.62% improved performance over the established baseline state-of-art model on the use-case; and 2) we leverage realistic SLA definitions for the use-case to achieve a dynamic SLA-aware oversight for scaling policy management with DRL.