Dynamic Resource Allocation for URLLC in UAV-Enabled Multi-access Edge Computing

Abstract

In the context of Ultra-reliable Low Latency communications (URLLC), the concepts of Multi-access Edge Computing (MEC), Network Function Virtualization (NFV), and Unmanned Aerial Vehicle (UAV) emerge as complementary paradigms that shall offer fine-grained on-demand distributed resources closer to the User Equipment (UE) and strong Line-of-Sight (LoS) paths between UAV and ground transmission nodes. However, compromise between onboard computation resource allocation and the URLLC requirements becomes challenging since UAVs are limited due to their size, weight, and power, and the virtualization adds extra overhead, which imposes a burden on the conventional Network Functions (NFs). This work proposes a NFV-MEC over UAV model based on Continuous-time Markov Chain (CTMC), with an embedded virtual resource scaling scheme for dynamic resource allocation (DRA). It also extensively analyzes the NFV-MEC architecture's virtualization layer, including node availability and power consumption, besides the URLLC conflicting reliability and latency metrics. The designed model allows analyzing how the main underlying virtualization parameters impact the critical services in a single NFV-MEC over a UAV node, assisting the network operator in proper node dimensioning and configuration.