QoS-Aware Service Prediction and Orchestration in Cloud-Network
  Integrated Beyond 5G
Mohammad Farhoudi Masoud Shokrnezhad Tarik Taleb
Novel applications such as the Metaverse have highlighted the potential of
beyond 5G networks, which necessitate ultra-low latency communications and
massive broadband connections. Moreover, the burgeoning demand for such
services with ever-fluctuating users has engendered a need for heightened
service continuity consideration in B5G. To enable these services, the
edge-cloud paradigm is a potential solution to harness cloud capacity and
effectively manage users in real time as they move across the network. However,
edge-cloud networks confront a multitude of limitations, including networking
and computing resources that must be collectively managed to unlock their full
potential. This paper addresses the joint problem of service placement and
resource allocation in a network-cloud integrated environment while considering
capacity constraints, dynamic users, and end-to-end delays. We present a
non-linear programming model that formulates the optimization problem with the
aiming objective of minimizing overall cost while enhancing latency. Next, to
address the problem, we introduce a DDQL-based technique using RNNs to predict
user behavior, empowered by a water-filling-based algorithm for service
placement. The proposed framework adeptly accommodates the dynamic nature of
users, the placement of services that mandate ultra-low latency in B5G, and
service continuity when users migrate from one location to another. Simulation
results show that our solution provides timely responses that optimize the
network's potential, offering a scalable and efficient placement.