Enhancing Edge Computing: DIHICLE Demonstrates Dynamic Workload Migration with FLUIDOS
The FLUIDOS project is excited to present the latest demo from the University of Patras, one of the winners of the first Open Call, showcasing an innovative solution for dynamic workload migration in edge computing environments.
Optimizing edge AI for drone-based powerline inspections
The DIHICLE solution is designed to support drone operations for powerline inspections, ensuring predictive maintenance in large-scale energy grids. As the drone moves across different coverage zones, FLUIDOS dynamically redistributes computational tasks to nearby edge servers, optimizing processing efficiency and reducing downtime.
Key Innovations in the Demo:
AI-Powered Fault Detection: A multi-layered microservice system captures and analyzes video footage from the drone to identify powerline issues.
Seamless Workload Migration: The system automatically detects and binds new edge servers as the drone moves, maintaining real-time AI inference.
Continuous Operation: If a connection is lost, FLUIDOS instantly reassigns tasks to an available provider, ensuring uninterrupted drone operations.
By leveraging FLUIDOS’ intelligent workload orchestration, DIHICLE provides a resilient and scalable solution for mission-critical edge computing applications.
Watch the full demo and explore FLUIDOS’ groundbreaking solutions on our YouTube channel.
Learn more about DIHICLE project, winner of the 1st Open Call.
