Edge Ai Orchestration Across the Edge-Cloud Continuum: From Monitoring to Adaptation

Abstract

The fast development of Edge AI has moved the intelligence towards distributed Edge-Cloud computing infrastructure moving away from centralized cloud in terms of low-latency, privacy-preserving, and context-aware applications. However, the deployment and management of the AI-based workloads in heterogeneous and resourceconstrained environments faces mounting complexity with the need for continuous and autonomous orchestration mechanisms. This paper provides a survey on Edge AI orchestration along the Edge-Cloud continuum based on a closed-loop lifecycle encompassing monitoring, decision making, placement, scaling, migration, and adaptation. We examine the ways in which modern systems sense dynamic environments, reason over multi-objective constraints, and perform runtime control actions to adapt and optimize performance, efficiency, and quality of service. Additionally, we systematically review state-of-the-art approaches starting from classic optimization, to emerging federated intelligence, autonomous agents, and foundation model-driven paradigms. Lastly, we identify key open challenges, including cross-layer monitors, explainable decision making, and continual adaptation under concept drift, outlining future research directions oriented towards fully autonomous and selfoptimizing Edge AI systems.