Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918159421800448 |
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| author | Jackman, Jack Ryan, David Narayanan, Arun Nardelli, Pedro Dey, Indrakshi |
| author_facet | Jackman, Jack Ryan, David Narayanan, Arun Nardelli, Pedro Dey, Indrakshi |
| contents | Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fog, and cloud compute with 5G URLLC (Ultra-Reliable Low-Latency Communications), SDN (Software Defined Networking), and NFV (Network Functions Virtualization) to co-optimize energy, latency, and reliability end-to-end. Our contributions are threefold: (i) a joint task offloading formulation that couples computation placement with network capacity under explicit URLLC constraints; (ii) a feasibility preserving, lightweight greedy heuristic that scales while closely tracking optimal energy and latency trade-offs; and (iii) a tiered AI (Artificial Intelligence) pipeline-reactive at the edge, predictive in the fog, strategic in the cloud-featuring privacy-preserving, federated GNNs (Graph Neural Networks) for fault detection and microgrid coordination. Unlike prior edge-only or cloud-only schemes, SDEN turns fragmented grid compute into a single, programmable substrate that delivers dependable, energy-aware, real time analytics establishing a first-ever, software defined path to practical, grid-scale CaaS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids Jackman, Jack Ryan, David Narayanan, Arun Nardelli, Pedro Dey, Indrakshi Systems and Control Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fog, and cloud compute with 5G URLLC (Ultra-Reliable Low-Latency Communications), SDN (Software Defined Networking), and NFV (Network Functions Virtualization) to co-optimize energy, latency, and reliability end-to-end. Our contributions are threefold: (i) a joint task offloading formulation that couples computation placement with network capacity under explicit URLLC constraints; (ii) a feasibility preserving, lightweight greedy heuristic that scales while closely tracking optimal energy and latency trade-offs; and (iii) a tiered AI (Artificial Intelligence) pipeline-reactive at the edge, predictive in the fog, strategic in the cloud-featuring privacy-preserving, federated GNNs (Graph Neural Networks) for fault detection and microgrid coordination. Unlike prior edge-only or cloud-only schemes, SDEN turns fragmented grid compute into a single, programmable substrate that delivers dependable, energy-aware, real time analytics establishing a first-ever, software defined path to practical, grid-scale CaaS. |
| title | Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.11286 |