Abstract
Edge computing devices have increased in number and capability over recent years. The ability to process data and execute machine learning in proximity to data generation and collection sources provides several advantages over using cloud- based data centers. We describe an orchestration mechanism that enables edge devices to make more effective use of energy resources in their proximity – a technique we refer to as “edge energy orchestration”. A software “orchestrator” can take account of renewable generation to alter how task execution on edge devices is carried out. An application scenario is used to illustrate the use of the orchestrator in practice, followed by a discussion about how this approach can be generalized to a broader set of applications
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Kumar, V. et al. (2025). Edge Energy Orchestration. In: Naldi, M., Djemame, K., Altmann, J., Bañares, J.Á. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2024. Lecture Notes in Computer Science, vol 15358. Springer, Cham. https://doi.org/10.1007/978-3-031-81226-2_17
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DOI: https://doi.org/10.1007/978-3-031-81226-2_17
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