QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G

TL;DR

This paper presents a novel framework combining non-linear programming and deep reinforcement learning to optimize service placement and resource allocation in edge-cloud networks for beyond 5G, ensuring ultra-low latency and service continuity.

Contribution

It introduces a joint optimization model and a DDQL-based prediction technique for dynamic service placement in B5G networks, addressing resource constraints and user mobility.

Findings

The proposed approach reduces latency and costs effectively.

It adapts to dynamic user behavior and mobility.

Simulation results demonstrate scalability and efficiency.

Abstract

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.