AI-driven Workload Management in Meta OS
Liubov Nedoshivina, Killian Levacher, Kieran Fraser, Anisa Halimi, Stefano Braghin · 2024
Properly leveraging resources within a continuum, and in particular the satisfaction of user requirements, requires deep understanding of the workload itself and its interaction with the infrastructure in which it is executed. We present a novel approach for workload placement and execution within the cloud-to-edge continuum that leverages advanced AI capabilities. These capabilities allow the presented approach to optimize workload allocation, both in terms of resources used, and the ability to satisfy explicit and implicit user defined service level requirements. We tested the approach against real-world datasets, demonstrating the advantages over a baseline approach.