2025 • In Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation: AI-Driven Industrial Transformation: Digital Leadership in Technology, Engineering, Innovation and Entrepreneurship, ICE 2025
AI; E2C Orchestrator; energy efficiency; Industry 5.0; resources optimisation; scalability; sustainability; Advanced technology; Allocation optimization; E2C orchestrator; Energy; Resources allocation; Resources optimization; Task allocation; Task distribution; Task efficiencies; Control and Optimization; Strategy and Management; Artificial Intelligence; Computer Science Applications; Information Systems and Management; Media Technology
Abstract :
[en] Modern industrial systems face several challenges in resource allocation, operational efficiency, and scalability, driven by the need for real-time data processing, and sustainable automation. The integration of advanced technologies, including edge-to-cloud (E2C) Artificial Intelligence (AI), digital twins, and blockchain, has created opportunities for innovation while increasing the complexity of orchestration and task distribution. This paper presents a novel E2C orchestrator that optimises task distribution across hybrid infrastructures, addressing the limitations of existing solutions such as static resource allocation and energy inefficiency. By leveraging AI-driven dynamic policies, the orchestrator increases efficiency, reduces latency and ensures robust data protection, in line with Industry 5.0s paradigm which emphasises human-centric and sustainable industrial ecosystems. Key outcomes include improved scalability, adaptability to dynamic workloads, and integration of advanced technologies for real-time responsiveness and operational visibility.
New Edge-to-Cloud Orchestrator for Intelligent Task Allocation and Efficiency Optimisation
Publication date :
2025
Event name :
2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)
Event place :
Valencia, Esp
Event date :
16-06-2025 => 19-06-2025
Main work title :
Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation: AI-Driven Industrial Transformation: Digital Leadership in Technology, Engineering, Innovation and Entrepreneurship, ICE 2025
Publisher :
Institute of Electrical and Electronics Engineers Inc.
The research leading to the results presented in this paper has received funding from the European Union’s funded Project TALON under grant agreement no 101070181.
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