###### Abstract
Consequently, the design principles of the ICN protocol emphasize flexibility, supporting variable-length encoding for packet sizes and field lengths, as well as allowing adjustments to packet sizes to suit various environments. Given the diverse scenarios in computing networks, the ICN protocol is more suitable than traditional network protocols, as it can accurately express users' personalized needs. Additionally, ICN utilizes a hop-by-hop routing mechanism based on names, which can be better optimized when combined with Software-Defined Networking [19] (SDN). When a service request arrives, routing based on the service name can be directly implemented in the data plane without needing prior knowledge of the service's deployment location. In contrast, in traditional networks, when a request arrives, it must be uploaded to the control plane for real-time processing before being forwarded through the data plane. This not only puts communication pressure on the control plane but also introduces additional latency for service discovery. Moreover, ICN's naming mechanism facilitates the unified management of numerous services, computing clusters, and network nodes. It is worth noting that for delay-sensitive services, information from computing clusters with sufficient resources but poor network conditions is unnecessary [20]. Therefore, network nodes can be divided into domains based on specific constraints, and service information is exchanged only within the domain. This approach not only reduces the network resources consumed by information exchange but also enhances system scalability. Based on the above analysis, this paper proposes an ICN-based delay-sensitive service scheduling architecture with the stateful data plane of SDN. The main contributions of this paper are as follows:  
1. A distributed delay-sensitive service scheduling architecture is proposed, utilizing ICN as the underlying network structure and employing SDN architecture for the network nodes. The control plane runs relevant algorithms to select candidate computing clusters, storing them in the data plane's state area. During the routing process of service requests, the corresponding candidate computing clusters are selected in the state area according to the requirements. This approach achieves load balancing at the network layer, enhances scheduling success rate, and lowers average delay. 2. Through a systematic analysis of the architecture and scheduling problem characteristics, this paper models the scheduling problem as a multi-criteria decision-making (MCDM) issue and proposes a novel algorithm combining the Technique for Order of Preference by Similarity to Ideal Solution [21] (TOPSIS) with the Best-Worst Method [22] (BWM). In particular, a priori consistency [23] is introduced in the BWM stage to ensure the scientificity and robustness of the weight selection stage. Compared to the traditional TOPSIS with the entropy weighting method(EWM), the proposed method improves scheduling performance and reduces algorithmic complexity. The rest of this paper is organized as follows: Section 2 introduces the related work, including the development of the Computing Network, the scheduling problems in the Computing Network, ICN network technology, the ICN-based Computing Network, and the SDN stateful data plane; Section 3 provides a detailed explanation of the architecture proposed in this paper; Section 4 thoroughly describes the algorithm implemented in this paper; Section 5 is the simulation experiment and result analysis; Section 6 summarizes the entire paper and discusses future prospects.