## 1 Introduction  
As artificial intelligence, big data, and other technologies continue to advance, computing-intensive and latency-sensitive tasks such as facial recognition, object detection, and autonomous driving have emerged in large numbers. This trend has driven the further development of technologies such as cloud computing and edge computing, enabling end-users to more conveniently access distributed computing resources at the edge. However, the deployment of edge computing still faces significant challenges. From a network perspective, the computing capabilities of individual nodes in edge computing scenarios are limited. Edge nodes cannot perceive each other and are unable to collaborate effectively. The challenge lies in the inability to efficiently schedule computing tasks to the optimal edge node. From a business requirements perspective, there is a need to decouple existing business application layers from the network. The application layer cannot accurately grasp the network's status. Addressing results dominated by the application layer may not achieve optimal overall performance and could potentially lead to network load imbalance. Services may not be efficiently arranged to the best edge node, resulting in impaired business operations [1]. Therefore, it is crucial to address how to efficiently and flexibly schedule computing resources between nodes, thereby improving the utilization of computing power [2; 3]. To more efficiently utilize ubiquitous distributed computing resources and expedite the processing of computing tasks, the convergence of computing and networking has become imminent. With the impetus from telecom operators and equipment vendors,the Computing First Networking (CFN) paradigm has emerged [4; 5]. CFN represents a new paradigm of cloud-network integration, aiming to achieve the interconnection and coordinated scheduling of ubiquitous distributed computing, storage, and other resources through the network. This enables on-demand and real-time invocation of massive computing and storage resources, facilitating global optimization and efficient utilization of computing and network resources. As businesses transition to the cloud and the consumer internet evolves into the industrial internet, the convergence of computing and networking has become a pivotal force in the digital transformation of social and economic activities. The CFN proposes a new network architecture and protocols, unifying the scheduling of computing and network resources, enabling global optimization and collaborative scheduling of these resources. Existing CFN architectures can be broadly categorized into two types: centralized architecture and distributed architecture. The centralized architecture, often combined with Software Defined Networking (SDN), separates the data plane from the control plane, allowing the controller to have a global view of computing and network resources for routing computation and decision-making. The distributed architecture achieves the synchronization of computing and network resource information through information exchanges between adjacent routing nodes, with each routing node handling forwarding and control decisions. Whether centralized or distributed, achieving comprehensive scheduling of computing resources across the entire network requires a routing algorithm that schedules CFN information. Similarly, the prerequisite for routing implementation is the establishment of a unified computing resource measurement and modeling mechanism. This paper introduces a centralized computing first network architecture, namely the Software Defined Computing First Network (SD-CFN). Leveraging the advantages of control-plane separation and centralized control, this architecture forms a global view of the computing resources and networking resources. Additionally, based on this architecture, we combine methods such as entropy weight and K-Means clustering to construct a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM).