Joint Optimization of Edge Computing Architectures and Radio Access Networks

TLDR

Virtualized radio access networks and multi‑access edge computing are key for the Tactile Internet and rising mobile traffic, yet their efficient deployment demands resource‑aware, demand‑driven design. This study introduces the MEC‑vRAN joint design problem, a modeling framework that simultaneously reduces vRAN costs and enhances MEC performance. The framework jointly selects base‑station function splits, fronthaul routing paths, and MEC placement, and is evaluated on three real‑world topologies with a face‑recognition service. Results show up to 2.5× cost savings versus non‑optimized RANs, though MEC‑driven function placement can raise overall network cost.

Abstract

Virtualized radio access network (vRAN) architectures and multiple-access edge computing (MEC) systems constitute two key solutions for the emerging Tactile Internet applications and the increasing mobile data traffic. Their efficient deployment, however, requires a careful design tailored to the available network resources and user demand. In this paper, we propose a novel modeling approach and a rigorous analytical framework, MEC-vRAN joint design problem (MvRAN), that minimizes vRAN costs and maximizes MEC performance. Our framework selects jointly the base-station function splits, the fronthaul routing paths, and the placement of MEC functions. We follow a data-driven evaluation method, using topologies of three operational networks and experiments with a typical face-recognition MEC service. Our results reveal that MvRAN achieves significant cost savings (up to 2.5 times) compared to non-optimized centralized RAN or decentralized RAN systems, and MEC pushes the vRAN functions to radio units and hence can increase substantially the network cost.