Jul 2026· International Scientific Conference on Information, Communication and Energy Systems and Technologies· pp. 139-152· 0 citations· 45 references
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.
The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns.
Fabio Orazio Mirto, Giuseppe Tricomi, L. D’Agati et al.· 0 citations
Cloud-edge computing environments are evolving rapidly, requiring orchestration mechanisms that may automatically construct and manage complex multi-step workflows with little human intervention. We introduce a framework for the agentic AI and how it should be able to orchestrate an autonomous end-to-end workload of cloud-edge enterprise infrastructures in general. The proposed framework relies on large language model (LLM)-driven agents capable of dynamic task decomposition, real-time decision-making, and self-correcting execution pipelines to manage heterogeneous workloads. Through the incorporation of multi-agent coordination protocols, context-aware scheduling algorithms, and feedback-driven optimization loops, the system facilitates seamless task delegation throughout edge nodes and cloud backend systems while managing latency, resource allocation, and compliance constraints. Experimental evaluations show up to percentage improvements in workflow completion rates, resource utilization, and fault tolerance over traditional static-command Rule-based orchestration approaches. Additionally, the framework features explainability modules and audit trails to promote transparency and accountability in autonomous operations. The results provide evidence that agentic AI architectures can serve as a scalable, resilient and intelligent control mechanism for next generation enterprise workflow management across hybrid cloud-edge settings. This has laid a foundation and is to our best of knowledge, the first systematic pioneers work that lays down a roadmap for production-grade autonomous orchestration deployed in analytics and enterprise domains.
Shiza Arshad, Anusha Joodala, A. Agade et al.· 2026 International Conferenc...· 0 citations
This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments and presents an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge.
Jennifer Clark· International Journal of Mac...· 0 citations
This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems, and reveals that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings.
A. Reza· International Journal of Mac...· 0 citations
CloudSkin aims to create a cognitive cloud-edge continuum platform that optimally exploits heterogeneous resources. Using AI, the platform automatically adapts to system and application behavior, and enables secure and seamless service deployment. Barcelona Supercomputing Center has led the design and development of an AI-based “Learning Plane” to automatically and continuously manage services in the cloudedge continuum to adapt to the dynamic environment. In the project, we enabled basic system models and workload characterization including regressions and time-series models, different levels of smart policies including heuristics and reinforcement learning, and we developed a data-connector agent to leverage those mechanisms towards real-world use cases. In this paper, we report on the technical results and insights we have explored within the project, explain how they are integrated as a learning plane to support CloudSkin use cases, and finally outline new open research lines.
Peini Liu, Joan Oliveras Torra, Marc Palacín et al.· Annual International Compute...· 0 citations
A research-driven conceptual framework for resilient edge-to-cloud AI architectures supporting distributed real-time decision making and identifies limitations associated with heterogeneous devices, uncertain ground truth, model drift, communication failures, and the absence of uniform evaluation criteria are identified.
Chinedu Eze, Fatima Bello· International Journal of Adv...· 0 citations