  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.
