Cross-Domain DRL Agents for Efficient Job Placement in the Cloud-Edge Continuum

The growing computational demands of modern applications call for resource management strategies that effectively utilize the strengths of both cloud and edge computing. Deep Reinforcement Learning (DRL) has shown great promise in addressing these challenges, offering advanced decision-making capabilities that optimize resource allocation and system performance. However, deploying DRL agents in cloud-edge continuum infrastructures remains a significant challenge due to their dependence on infrastructure-specific state-action representations. This paper presents a novel architectural framework for DRL agents that incorporates feature extraction and adaptation mechanisms to enable their seamless operation across diverse environments. By transforming state features into an infrastructure-agnostic representation, our approach reduces the need for extensive retraining when system configurations change. Experimental results show that our method outperforms both a heuristic method and a DRL baseline algorithm while achieving faster convergence when infrastructure and workloads change. This work is an important step forward in developing transferable and adaptable DRL solutions for real-world cloud-edge resource management challenges.