MLOps meets Edge Computing: an Edge Platform with Embedded Intelligence towards 6G Systems
Nikos Psaromanolakis, Vasileios Theodorou, Dimitris Laskaratos, Ioannis Kalogeropoulos, Maria-Eleftheria Vlontzou, Eleni Zarogianni, Georgios Samaras
Abstract
The evolution towards more human-centered 6G networks requires the extension of network functionalities with advanced, pervasive automation features. In this direction, cloud-native, softwarized network functions and integration of extreme/far edge devices shall be supported by even more distributed and decomposable systems, such as Edge Cloud environments, while building on AI/ML data-driven mechanisms to improve their performance and resilience for the stringent requirements of next-generation applications. In this work, we propose an intelligence-native Edge Management Platform coupled with MLOps functionalities-the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge Platform-which encompasses automation features for cloud-native lifecycle management of Edge Services. Our introduced architecture incorporates MLOps services and processes, operating as integrated micro-services with the rest of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge architectural components, ensuring the reliable operation and QoS of Edge network and application services. We experimentally validate our approach with a prototypical implementation of key <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge features, including the incorporation of state-of-the-art ML models for performance prediction and anomaly detection, on a multi-media streaming use case based on scenarios from real production environment.