Ainur: A Framework for Repeatable End-to-End Wireless Edge Computing Testbed Research

TL;DR

Ainur is a hardware-agnostic, cloud-native framework that automates wireless edge computing testbeds, simplifying deployment and management of complex experiments across diverse scenarios.

Contribution

It introduces the first hardware-agnostic, end-to-end wireless testbed automation framework built on cloud-native technologies, enhancing flexibility and reproducibility.

Findings

Demonstrates flexible configuration of physical links and computation placement.

Showcases automation of complex experimental scenarios.

Provides an open-source platform for wireless edge computing research.

Abstract

Experimental research on wireless networking in combination with edge and cloud computing has been the subject of explosive interest in the last decade. This development has been driven by the increasing complexity of modern wireless technologies and the extensive softwarization of these through projects such as a Open Radio Access Network (O-RAN). In this context, a number of small- to mid-scale testbeds have emerged, employing a variety of technologies to target a wide array of use-cases and scenarios in the context of novel mobile communication technologies such as 5G and beyond-5G. Little work, however, has yet been devoted to developing a standard framework for wireless testbed automation which is hardware-agnostic and compatible with edge- and cloud-native technologies. Such a solution would simplify the development of new testbeds by completely or partially removing the requirement for custom management and orchestration software. In this paper, we present the first such mostly hardware-agnostic wireless testbed automation framework, Ainur. It is designed to configure, manage, orchestrate, and deploy workloads from an end-to-end perspective. Ainur is built on top of cloud-native technologies such as Docker, and is provided as FOSS to the community through the KTH-EXPECA/Ainur repository on GitHub. We demonstrate the utility of the platform with a series of scenarios, showcasing in particular its flexibility with respect to physical link definition, computation placement, and automation of arbitrarily complex experimental scenarios.