## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies
The latter denotes the average value of the preference-associated priority indicators on the CRNs occupied by successfully scheduled request packets. When the number of successfully scheduled request packets is the same, the magnitude of the average value of priority indicators reflects the capability of the computing resource scheduling strategies to meet diverse user demands and preferences. ### Success Rate of Scheduling

In the context of Computing Network, reference [56] compares three scheduling schemes: CFN-dyncast, Native DNS scheduling, and Compute-Aware DNS scheduling, CFN-dyncast directs requests to a single \"optimal\" CRN based on the status information of both computing and network resources. The Native DNS scheduling scheme guides requests to various CRNs according to statically configured weights. Meanwhile, the Compute-Aware DNS scheduling scheme dynamically adjusts weights based on the status information of resources, facilitating dynamic load balancing. It is noteworthy that these schemes operate within a flat architecture and rely on the traditional TCP/IP network for scheduling. This simulation experiment compares the three aforementioned schemes with the IPCRSA. To ensure comparability, the same simulation topology, as shown in Figure 7, is employed for all three schemes, treating the hierarchical topology as a flat topology for scheduling. In terms of specific scheduling strategies, IPCRSA utilizes the computing resource scheduling strategy corresponding to the default preference. This involves scoring each CRN based on its load status and subsequently selecting the CRN using the roulette-selection algorithm. Under the same scoring method, the CFN-dyncast scheme directly chooses the CRN with the highest score. The Compute-Aware DNS scheduling scheme adopts the same scheduling strategy as IPCRSA, achieving dynamic weight adjustments. The Native DNS scheduling scheme defaults to equal static weights, i.e., round-robin scheduling. However, in contrast to the scheduling of service requests in [56], the candidate set of CRNs for basic resource requests in service deployment scenarios undergoes high dynamic changes, making it challenging to perform effective scheduling based on static weights. Additionally, considering the similarity in the performance of random scheduling and round-robin scheduling when the number of requests is large, this experiment replaces the static-weight setting method with a random selection method for scheduling. Finally, the performance of the four schemes is compared under the Scheduling_Delay of \(10\,ms/50\,ms/100\,ms/150\,ms\). Figure 8 present the scheduling performance comparison of the four schemes under different scheduling delay constraints. It can be observed from the figures that, regardless of the scheduling delay constraint, IPCRSA consistently achieves an overall higher scheduling success rate compared with the other three schemes. The advantage of IPCRSA becomes more pronounced when the number of computing resource requests is relatively small. Particularly, when the total number of requests is \(5\,w\), IPCRSA demonstrates a minimum improvement of 24% and a maximum improvement of 63% in scheduling success rate compared with those of the other three schemes. As the number of requests increases, resource competition intensifies, and constrained by the total available resources of CRNs, the scheduling success rates of all four schemes decrease. However, IPCRSA maintains its advantageous position. These conclusions thoroughly demonstrate the superiority of IPCRSA in improving scheduling success rates, primarily attributed to its on-path computing resource scheduling mechanism. This mechanism enables computing resource request packets to dynamically adjust their scheduling destinations during the routing process based on changes in resource status information, enhancing the flexibility of routing and scheduling. Figure 8: \(Cont\). Additionally, this experiment analyzes the impact of different scheduling delay constraints on the scheduling success rate of IPCRSA.