Edge-Oriented Orchestration of Energy Services Using Graph-Driven Swarm Intelligence
This addresses the need for low-latency, decentralized energy service orchestration in smart grids with IoT devices, though it appears incremental as it combines existing techniques like graph models and swarm intelligence.
The paper tackles decentralized orchestration of energy services in smart grids by proposing a unified edge-fog-cloud framework with a graph-based data model and swarm-based heuristic algorithm. Validation with a real-world KubeEdge deployment demonstrated zero downtime service migration under dynamic workloads while maintaining service continuity.
As smart grids increasingly depend on IoT devices and distributed energy management, they require decentralized, low latency orchestration of energy services. We address this with a unified framework for edge fog cloud infrastructures tailored to smart energy systems. It features a graph based data model that captures infrastructure and workload, enabling efficient topology exploration and task placement. Leveraging this model, a swarm-based heuristic algorithm handles task offloading in a resource-aware, latency sensitive manner. Our framework ensures data interoperability via energy data space compliance and guarantees traceability using blockchain based workload notarization. We validate our approach with a real-world KubeEdge deployment, demonstrating zero downtime service migration under dynamic workloads while maintaining service continuity.