SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

A Computing Power Network (CPN) is a cloud–edge–end integrated infrastructure that interconnects heterogeneous computing resources to support latency-sensitive and volatile workloads. A core challenge in CPN task scheduling is to jointly determine task prioritization and computational node selection under dynamic arrivals, resource heterogeneity, and multi-objective conflicts. In this paper, we study dynamic task scheduling in a CPN and aim to (i) minimize average task completion time, (ii) maximize scheduling success rate under deadline constraints, and (iii) improve global resource utilization. We propose CPN-HRL, a hierarchical deep reinforcement learning framework that decomposes scheduling into two coordinated subproblems: a high-level agent (LSTM-PPO) generates global priority weights for queued tasks, and a low-level agent (GAT-PPO) performs topology-aware node selection for each task in the ranked sequence. To further guarantee timeliness for latency-critical tasks, we introduce a two-queue mechanism with an Urgent Queue and a Main Queue, together with a dynamic reordering trigger. Simulation results on multiple real-world topologies show that, under our experimental settings, CPN-HRL reduces average completion time and maintains high scheduling success rate under high loads, while improving overall resource utilization.

The space-air-ground integrated network (SAGIN) is formed by the fusion of ground and aircraft networks. It overcomes the limitations in communications that cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, the existence of many heterogeneous devices in SAGIN poses significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also affect the quality of service (QoS) for users. In this context, this study proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a software-defined network (SDN), network function virtualization (NFV), and mobile edge computing (MEC), aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize operator deployment costs while ensuring QoS, this study formulates a resource scheduling optimization model tailored for SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm named DRL-G, which is based on heuristics and deep reinforcement learning (DRL), aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results show that SAGIN-MEC can reduce the end-to-end delay by 6-15 ms compared to the terrestrial edge network. In addition, compared to other algorithms, DRL-G can improve the service request reception rate by up to 28%.