D3NN: Adaptive Partitioning and Cross-Tier Resource Orchestration for Cloud–Edge Collaborative Inference

Abstract

Deep Neural Networks (DNNs) have become foundational to intelligent systems, yet deploying them efficiently under strict latency, resource, and privacy constraints remains challenging. While cloud-only inference suffers from transmission latency and privacy risks, and edge-only execution is limited by hardware capacity, cloud–edge collaborative inference offers a practical middle ground by combining the cloud’s compute strength with the edge’s proximity to data sources for low-latency, scalable, and privacy-aware inference. However, realizing this potential requires adaptive DNN partitioning that responds to dynamic workloads and network conditions, as well as fine-grained cross-tier resource orchestration to avoid bottlenecks and ensure system stability. To this end, we propose DDPG-DRPA-driven Deep Neural Network(D3NN), a novel and efficient framework for partitioned DNN deployment across cloud and edge resources. We formulate the pipeline partitioning of DNNs as a Markov Decision Process (MDP). A value function is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm, and a Dynamic Resource Partitioning Agent (DRPA) allocates suitable cloud or edge resources to each DNN layer according to specific task types. As a result, D3NN adapts dynamically to both environmental conditions and task requirements. Under maximum task arrival rate scenarios, our approach reduces inference latency by 13.7% compared to pure cloud-based inference and by 33.5% compared to pure edge-based inference, demonstrating its practical effectiveness in resource-constrained cloud–edge systems.