Directives for Function Offloading in 5G Networks Based on a Performance Characteristics Analysis
Falk Dettinger, Matthias Wei{\ss}, Daniel Baumann, Martin Sommer, Michael Weyrich

TL;DR
This study evaluates 5G non-standalone networks for vehicle function offloading, analyzing latency, packet delivery, and transfer times across different deployment strategies and locations in real-world scenarios.
Contribution
It provides empirical insights into the performance of 5G offloading for vehicle algorithms, highlighting conditions under which cloud offloading is feasible.
Findings
Average signal quality of 84% with no connectivity interruptions.
Packet Error Rate below 0.1% for tested algorithms.
Transfer times vary with location and network connection, processing times depend on hardware.
Abstract
Cloud-based offloading helps address energy consumption and performance challenges in executing resource-intensive vehicle algorithms. Utilizing 5G, with its low latency and high bandwidth, enables seamless vehicle-to-cloud integration. Currently, only non-standalone 5G is publicly available, and real-world applications remain underexplored compared to theoretical studies. This paper evaluates 5G non-standalone networks for cloud execution of vehicle functions, focusing on latency, Round Trip Time, and packet delivery. Tests used two AI-based algorithms -- emotion recognition and object recognition -- along an 8.8 km route in Baden-W\"urttemberg, Germany, encompassing urban, rural, and forested areas. Two platforms were analyzed: a cloudlet in Frankfurt and a cloud in Mannheim, employing various deployment strategies like conventional applications and containerized and…
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