Enabling efficient collection and usage of network performance metrics at the edge

Abstract

Microservices (MSs)-based architectures have become the de facto standard for designing and implementing edge computing applications. In particular, by leveraging Network Performance Metrics (NPMs) coming from the Radio Access Network (RAN) and sharing context-related information, AI-driven MSs have demonstrated to be highly effective in optimizing RAN performance. In this context, this work addresses the critical challenge of ensuring efficient data sharing and consistency by proposing a holistic platform that regulates the collection and usage of NPMs. We first introduce two reference platform architectures and detail their implementation using popular, off-the-shelf database solutions. Then, to evaluate and compare such architectures and their implementation, we develop PACE, a highly configurable, scalable, MS-based emulation framework of producers and consumers of NPMs, capable of realistically reproducing a broad range of interaction patterns and load dynamics. Using PACE on our cloud computing testbed, we conduct a thorough characterization of various NPM platform architectures and implementations under a spectrum of realistic edge traffic scenarios, from loosely coupled control loops to latency- and mission- critical use cases. Our results reveal fundamental trade-offs in stability, availability, scalability, resource usage, and energy footprint, demonstrating how PACE effectively enables the identification of suitable platform solutions depending on the reference edge scenario and the required levels of reliability and data consistency.