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 multiagent 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 HeteroGraph 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 toavoid network bottlenecks while leveraging edge/cloud resources in sparse regions. Ablation studies confirmthat collective information integration is essential for learning such context-aware policies, with IDHGQL consistently outperforming static baselines across all evaluated metrics.