HiPEAC 2025 ML4ECS Workshop - CODECO Demos

Autonomic end-to-end system management across the cloud-edge continuum is becoming increasingly critical as computational demands, scale, dynamics, and system heterogeneity grow rapidly. Machine Learning for Edge-Cloud Systems (ML4ECS) is a full-day workshop that focuses on academic and industrial innovation and research on developing and deploying AI/ML models to enable optimized system management and application execution on the continuum. The ML4ECS workshop aims to bring together a community of researchers and practitioners who study problems at the intersection of ML, Edge-Cloud continuum, distributed and dependable system operation, and flexible, adaptive, and resilient application deployment. It seeks to reach a broader community invested in this fast-evolving field, aiming to increase awareness of ongoing initiatives, encourage collaboration, and promote the open exchange of ideas. CODECO organized the workshop together with projects HE EDGELESS and MLSYSOPS. In the workshop, CODECO provided three demos: The CODECO Experimentation Framework, by Georgio Koukis, Lefteris Mamatas, Vasilis Tsaoussidis, and Panagiotis Karamolegkos presented CODECO, an experimentation framework designed to optimize edge-cloud deployments, enhancing performance and resource management. AI-based Scheduling in Edge-Cloud: The CODECO PDLC Approach, by Alejandro Espinoso and Rizkallah Touma introduced an AI-powered scheduling strategy that optimizes application placement across edge and cloud environments, improving efficiency and responsiveness. Energy-Aware Edge-Cloud Orchestration in CODECO Dalal Ali and Rute C. Sofia explored how energy efficiency can be integrated into edge-cloud orchestration within the CODECO project, aiming for sustainable and cost-effective computing.