A Channel-Aware FL Approach for Virtual Machine Placement in 6G Edge intelligent Ecosystems

This paper deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machine (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the paper is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The paper focuses on the following main issues: i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; ii) use of an AI-based channel conditions forecasting module at the beneits of the FL process; iii) development of a suitable VMs placement on the basis of their popularity and of an eicient tasks allocation technique based on a modified version of the auction theory and a proper matching game; iv) enhancement of the system reliability by an echo-state-network, located on each computation node and running in background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.