###### Abstract  
The Computing Network is an emerging paradigm that integrates network and computing resources. One of its goals is to satisfy the requirements of delay-sensitive services through network scheduling capabilities. However, traditional TCP/IP networks are deficient in accurately being aware of requirements and performing flexible routing based on service levels. Information-Centric Networking (ICN) addresses these issues through its flexible protocol design and content-based routing mechanism. Additionally, the integration of Software-Defined Networking (SDN) technology further enhances its routing flexibility. Therefore, this paper proposes an ICN-based delay-sensitive service scheduling architecture with an SDN stateful programmable data plane. The network nodes are first layered based on the type of computing clusters they are linked with, and then within each layer, they are divided into several domains according to delay constraints. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, combined with the Best-Worst Method (BWM) weighting method, is adopted to evaluate the candidate clusters, and the corresponding scheduling strategy is executed in the stateful programmable data plane. The simulation results show that compared with other scheduling architectures and traditional TOPSIS with the Entropy Weight Method (EWM), the proposed architecture and algorithm show significant advantages in reducing the overall delay of service requests and improving the scheduling success ratio, as well as the load balance of the computing clusters. Computing Network; ICN; delay-sensitive service scheduling; SDN; stateful data plane 20241410207103390/20  
clusters (potentially in forms such as virtual machines or containers). The network fulfills user requests based on the quality of services offered, allowing users to simply state their needs (e.g., a response within 10 ms). The emphasis is on meeting user requirements. And this paper focuses on the second scenario. Current research extends cloud and edge computing resource scheduling to the Computing Network, primarily focusing on resource allocation for service placement or non-delay-sensitive services [6; 7; 8; 9; 10]. However, for delay-sensitive requests, the aforementioned scheduling approaches are not suitable, as these requests are more concerned with timely completion rather than resource allocation [11; 12]. Therefore, further research into delay-sensitive service scheduling beyond bare-metal resources is essential. Currently, few studies address delay-sensitive service scheduling in the Computing Network [13; 14; 15]. Existing research tends to focus on single aspects: some propose optimization algorithms without practical implementation [14; 15], while others emphasize architectural design but ignore detailed performance evaluation [13]. A common limitation is their reliance on traditional TCP/IP networks, which present several issues for delay-sensitive scheduling [16]. Specifically, the layered design separates services from the network, diminishing the network's ability to accurately sense service requirements, while the best-effort transmission mechanism introduces latency uncertainty. Additionally, most research relies on centralized architectures. As the number of services grows, the overhead of service information notifications leads to excessive network resource consumption, which will disrupt normal traffic flow. Information-Centric Networking [17] (ICN) offers significant benefits for scheduling delay-sensitive services in the Computing Network. Firstly, the ICN protocol was designed by learning from the lessons of TCP/IP networks [18] (for example, the exhaustion of IPv4 addresses led to the introduction of IPv6, and the excessive overhead of IPv6 resulted in the design of 6 LoWPAN).