## 1 Introduction  
The development of the digital economy has driven the generation of massive data, and the processing of this vast amount of data relies on powerful computing resources and widely covered network connections. The convergence of computing and network has become a crucial trend. Against this backdrop, a novel network paradigm, termed the \"Computing Network\", has been proposed [1; 2; 3], which integrates computing resource scheduling capabilities into the network. This paradigm aims to interconnect distributed computing resource nodes; coordinate computing resources, network resources, and user requirements; and achieve the integrated scheduling of computing and network resources [4]. Various network service providers, represented by telecommunications operators, have proposed their own Computing Network architectures. Each provider has put forth their respective ideas regarding the collaboration of computing and network resources, and they have implemented computing resource scheduling in either a centralized or distributed manner within these architectures [5; 6; 7; 8]. It is important to note that computing resource scheduling in Computing Network is a broad concept. It can involve the scheduling of fundamental resources such as CPU, memory, and storage, as well as the scheduling of specific computing services provided on computing resource nodes. However, this paper primarily focuses on the scheduling of basic resources under service deployment requests [9]. Furthermore, computing resource scheduling, as one of the critical issues in computational systems such as Computing Network, Edge Computing, and Cloud Computing, plays a crucial role in determining the effectiveness of computing resource utilization and user service experience [10]. However, with the rapid increase in cloud-edge-end computing resource nodes, along with diverse computing demands from users, the centralized computing resource scheduling mechanism in a Computing Network faces scalability limitations. It is not suitable for large-scale network deployments [11; 12; 13]. Distributed scheduling methods, on the other hand, offer advantages in terms of scalability and reliability [14]. Therefore, this paper places emphasis on the study of distributed computing resource scheduling architectures. Existing distributed resource scheduling architectures in a Computing Network mostly rely on a flat architecture, performing full-scale dissemination of resource status information and scheduling among all the scheduling nodes [12]. Faced with a scenario of high-frequency changes in computing resource node status, it is challenging for resource status information to synchronize rapidly [15; 16]. Delayed resource status information is prone to scheduling failures. Moreover, current distributed resource scheduling architectures perform scheduling based on conventional TCP/IP networks, determining the final scheduling destination at the user entry node. This \"in-out\" scheduling method lacks flexibility [13] and cannot effectively address resource competition issues in distributed resource scheduling scenarios, leading to potential scheduling failures. In contrast, in Information-Centric Networking (ICN), the content-name-based routing mechanism, combined with Software-Defined Networking (SDN) technologies, allows for convenient readdressing of routing in the network layer [17; 18; 19], offering high routing flexibility. Therefore, ICN possesses advantageous conditions for implementing dynamic computing resource scheduling. Additionally, current resource scheduling architectures based on traditional TCP/IP networks often adopt single and static scheduling strategies, such as optimizing the utilization and load balancing of computing resources or minimizing resource usage costs [20]. These architectures do not specifically design scheduling schemes based on user preferences, thus failing to meet diverse user requirements.