Delay and reliability-constrained VNF placement on mobile and volatile 5G infrastructure

TL;DR

This paper addresses the complex problem of placing Virtual Network Functions in 5G networks with strict delay and reliability requirements, considering mobility and resource constraints, proposing a heuristic solution evaluated through detailed simulations.

Contribution

It introduces a novel heuristic for VNF placement that accounts for mobility, battery limitations, and reliability in volatile 5G environments, optimizing for cost and performance.

Findings

Heuristic effectively manages mobility and resource constraints.

Simulation results show significant cost reduction.

Improves service reliability in dynamic 5G scenarios.

Abstract

The ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the automation and optimization of the software-defined, virtualization-based next generation of networks such as 5G and beyond. Network services provided for autonomous vehicles, factory automation, e-health and cloud robotics often require strict delay bounds and reliability constraints, which are influenced by the location of its composing Virtual Network Functions. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. Moreover, these devices are continuously on the move and might lose network connection or run out of battery power, which further challenge service delivery in this dynamic environment. This work tackles the mobility, and battery restrictions; as well as the temporal aspects and conflicting traits of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a cost-minimizing Virtual Network Function placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios.