## 2 Related Works
### Computing Network and Computing Resource Scheduling  
In recent years, the academic and industrial sectors have conducted extensive research and exploration on the Computing Network. On a macro-policy level, Chinese telecommunications operators have issued a white paper on Computing Network research, elaborating on the demands, architecture, and key technologies of the Computing Network. Various Computing Network architectures, including Computing-aware Networking (CAN), Computing Power Network (CPN), Computing First Network (CFN), etc., have been proposed [23; 24; 25]. In terms of standardization, ITU-T has initiated and released a series of standards related to the Computing Network, such as Y.CPN-arch, Y.IMT2020-CAN-req, Q.CPN, Q.BNG-INC, etc. [26; 27; 28; 29]. The IETF's Routing Area Working Group (RTGWG) working group has conducted research on CFN, submitting three core proposals that outline the framework, requirements, and use cases of CFN [1; 7; 30]. Additionally, the IRTF has established the Computing in the Network Research Group (COINRG) to conduct relevant research on computing-network integration standards. On the academic front, the authors in [2] first proposed a Computing Network solution based on deep integration of cloud, network, and edge, providing an implementation case for AI applications. The authors in [3] analyzed the research background and key technologies of the Computing Network, demonstrating their advantages in 6G business scenarios. The authors in [11] provided a detailed exposition of the overall composition and routing protocols of the Computing Network architecture: CFN. As the core of the Computing Network, computing resource scheduling in it has also received extensive attention. In terms of distributed scheduling, the authors in [8] proposed a computing-aware bandwidth allocation method called CABA based on utility optimization. CABA selects the optimal computing resource node and routing path for resource requests in the Computing Network, enhancing the overall utilization of computing resources. Although this method considered user satisfaction as one of the decisive factors in the scheduling of computing resources, it failed to account for the users' preferences for resource requirements. The authors in [12] introduced a CAN-based Computing Network simulation prototype, validating the effectiveness of its computing-aware routing scheduling mechanism and improving the overall utilization of computing resources. The authors in [13] proposed a Computing Network routing protocol, CARP. It comprehensively considered computing and network resource information, scheduled computing resource requests to optimal service nodes, and optimized system capacity and end-to-end average latency. However, both methods above assumed that the resource status information they acquired was real-time, overlooking the resource competition issue in the scenario of distributed scheduling, which was challenging to achieve in real-world network environments. Moreover, the method of always choosing a single optimal destination resource node, in the absence of real-time information synchronization, could not achieve load balancing among nodes, potentially adversely affecting resource scheduling. Additionally, both methods above were based on the TCP/IP network for scheduling and determined the destination resource node at the ingress node, lacking the flexibility to adjust based on resource status information in a timely manner. The authors in [31] presented a Computing Network resource scheduling architecture centered around the IP network, implementing computing orchestration and scheduling at the network layer based on the Segment Routing IPv6 (SRv6). The authors in [32] built a CPN prototype experimental platform based on Kubernetes, achieving dynamic allocation of computing resources and flexible scheduling of resource requests, reducing response latency, and enhancing load balancing performance.