科研成果详情

To address the limitations of ARM64-based AI edge devices, which are energy-efficient but computationally constrained, as well as general-purpose edge servers, this paper proposes a multi-modal CollaborativeHeterogeneous Edge Computing (CHEC) architecture that achieves low latency and enhances computational capabilities. The CHEC framework, which is segmented into an edge private cloud and an edge public cloud, endeavors to optimize the profits of Edge Service Providers (ESPs) through dynamic heterogeneous resource management. In particular, it is achieved by formulating the challenge as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. We introduce a resource collaboration system based on resource leasing incorporating three Economic Payment Models (EPMs), ensuring efficient and profitable resource utilization. To tackle this complex issue, we develop a three-layer Hybrid Deep Reinforcement Learning (HDRL) algorithm with EPMs, HDRL-EPMs, for efficient management of dynamic and heterogeneous resources. Extensive simulations confirm the algorithm's ability to ensure convergence and approximate optimal solutions, significantly outperforming existing methods. Testbed experiments demonstrate that the CHEC architecture reduces latency by up to 21.83% in real-world applications, markedly surpassing previous approaches.