Decentralized Orchestration Framework for Distributed AI Deployments Across Fluid Computing Environments

Fluid Computing has emerged as a promising paradigm for enhancing massive and heterogeneous resource management across the Cloud-to-Edge continuum for Internet of Things (IoT) and artificial intelligence (AI) applications. Despite its advantages, research into the optimal deployment of distributed applications across fluid scenarios remains scarce, and existing centralized frameworks cannot exploit emerging AI-native features nor model realistic multi-domain deployment scenarios. This paper presents an innovative provider-based architecture for the optimal orchestration of distributed AI services in fluid environments under 6G network capabilities. The proposed hybrid solution includes robust and scalable decentralized orchestration for the placement of cross-provider workloads without a central broker, as well as leveraging autonomous decision-making devices through distributed task offloading techniques. The proposal was tailored to a Decentralized Federated Learning deployment, serving as a use case in settings with strict privacy and security requirements. This approach was adopted to illustrate the viability of the proposal by deploying large distributed AI services in 6G-like networks.