Heterogeneous GNN for collective-task offloading in cloud-edge via deep Q-learning

Task offloading in edge-cloud computing systems requires determining optimal allocation of application components across heterogeneous infrastructure while balancing multiple objectives, like energy consumption, latency, or cost. This problem becomes particularly complex in large-scale deployments (e.g., smart cities, industrial IoT) where existing approaches fail to address collective phenomena, namely emergent system-wide behaviors like network congestion that arise from multi-device interactions, leading to suboptimal offloading decisions in large-scale deployments. To address these challenges this paper introduces a multi-agent learning framework for collective component offloading that decomposes applications into a directed acyclic graph of macro-components, enabling partial offloading where individual components can be selectively executed locally or migrated to edge/cloud servers. Our system model represents the infrastructure as a heterogeneous graph of application devices and infrastructure nodes, supporting decentralized offloading decisions while maintaining component interdependencies. In particular, we propose Informed Deep Hetero Graph Q-Learning (IDHGQL), which combines: (1) Heterogeneous Graph Neural Networks (HeteroGNNs) for policy representation that naturally handle diverse device types and relationships; (2) Aggregate computing to enrich device observations with collective system state information; and (3) a multi-agent Deep Q-Learning algorithm based on centralized training with decentralized execution that balances individual constraints with emergent collective phenomena. Experimental evaluation demonstrates IDHGQL’s effectiveness in multi-objective optimization scenarios, successfully learning policies that balance battery consumption, latency, and infrastructure costs. In density-aware scenarios, agents learn spatially-adaptive strategies that dynamically adjust offloading decisions based on local congestion: favoring local execution in high-density areas to avoid network bottlenecks while leveraging edge/cloud resources in sparse regions. Ablation studies confirm that collective information integration is essential for learning such context-aware policies, with IDHGQL consistently outperforming static baselines across all evaluated metrics.