###### Abstract  
With the rapid development of wireless networks, wireless edge computing networks have been widely considered. The heterogeneous characteristics of the 6G edge computing network bring new challenges to network resource scheduling. In this work, we consider a heterogeneous edge computing network with heterogeneous edge computing nodes and task requirements. We design a software-defined heterogeneous edge computing network architecture to separate the control layer and the data layer. According to different requirements, the tasks in heterogeneous edge computing networks are decomposed into multiple subtasks at the control layer, and the edge computing node alliance responding to the tasks is established to perform the decomposed subtasks. In order to optimize both network energy consumption and network load balancing, we model the resource scheduling problem as a Markov Decision Process (MDP), and design a Proximal Policy Optimization (PPO) resource scheduling algorithm based on deep reinforcement learning. Simulation analysis shows that the proposed PPO resource scheduling can achieve low energy consumption and ideal load balancing. edge computing; deep reinforcement learning; software-defined network; heterogeneous network; resource scheduling +
Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote †: journal: Applied Sciences  
network becomes a data layer and control layer decoupling network architecture, and the control logic of the nodes is transferred to the upper centralized controller to provide flexible network management and scheduling. Software-defined edge computing networks can effectively solve the problems of traditional heterogeneous edge computing network resource scheduling and network heterogeneity. Through the centralized control of the control layer, the heterogeneous edge computing network can effectively respond to the heterogeneous task requirements and match the task types with the edge computing devices. At the same time, the architecture of separation of control layer and data layer can achieve efficient reuse of the underlying physical network resources. In addition, the software-defined edge computing network allows edge computing devices with different parameter functions to coexist, and has good adaptability to node heterogeneity. The work in [9] focused on a two-tier edge computing system composed of a macro base station and multiple micro base stations and jointly optimized task scheduling and energy management decisions. In terms of software-defined network, the work in [10] proposed an software-defined network architecture of wireless networks and applied it to wireless edge computing networks. The enhancing performance of software-defined wireless edge computing networks was verified. Further, the work in [11] proposed the separation of wireless network control layer and data layer. In this network, the nodes only transmitted data without participating in data processing which improved the efficiency and life of the network. Considering edge computing, the work in [7; 12; 13] analyzed the advantages of edge computing and software-defined network in mobile vehicle networking. The architecture of software-defined vehicle networking greatly improves the flexibility and reliability of the network. The work in [14] summarized the application of software-defined network and edge computing in the next generation mobile communication. It was shown that the Internet of things based on software-defined networks and edge computing can effectively shorten the service response time and improve the quality of users. It is worth noting that most of the work [10; 11; 15; 16; 17] did not take into account the heterogeneous characteristics of data and edge computing devices in 6G networks. The optimization and scheduling problems in heterogeneous edge computing networks remain to be further studied.