## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies
The results from Figure 9 indicate that as the constraints on scheduling delay gradually loosen, the scheduling success rate of IPCRSA increases accordingly. The reason behind this observation is that relaxed scheduling delay constraints provide computing resource request packets with more opportunities for matching, processing, forwarding, and rescheduling, thereby enhancing the scheduling success rate. ### Average Value of Priority Indicators Corresponding to Preferences

To validate the scheduling performance of the preference-based computing resource scheduling strategies in IPCRSA, this simulation experiment takes the examples of two strategies corresponding to two user preferences: Efficiency-First and Cost-First (referred to as the Efficiency-First strategy and Cost-First strategy, respectively). These strategies are implemented in the simulated topology shown in Figure 7 and compared against the random-scheduling strategy. The total number of computing resource request packets is set to \(10\,w\), with a Scheduling_Delay of \(100\,ms\). The vital comparative metrics are the average

Figure 8: Success rate of scheduling with different Scheduling_Delay. Figure 9: Success rate of scheduling in IPCRSA with different Scheduling_Delay. values of the priority indicators corresponding to the above two user preferences, i.e., the average PUE and the average usage price. Figure 10 presents the average PUE of CRNs occupied by requests under the Efficiency-First strategy and random-scheduling strategy, with successfully scheduled packet quantities of \(1\,w/3\,w/5\,w\). The figure shows that the Efficiency-First strategy achieves an overall lower average PUE compared with the random-scheduling strategy. Particularly, when the number of successfully scheduled requests reaches \(1\,w\), the average PUE of occupied CRNs is reduced by up to \(31\%\). Similarly, Figure 10 provides a comparison of the Cost-First strategy and random-scheduling strategy. It shows that the Cost-First strategy obtains a lower overall CRN usage price compared with the random-scheduling strategy. Especially with \(1\,w\) successfully scheduled packets, the average usage price is reduced by approximately \(28\%\). Regarding scheduling delay, Figure 11 reveals that although the average scheduling delay for both Efficiency-First and Cost-First strategies is slightly higher



The conclusions above strongly demonstrate the advantages of the preference-based computing resource scheduling strategies in IPCRSA. It simultaneously takes into account Users' preferences for resource demands and the load balancing of resource scheduling, meeting diverse user requirements with low scheduling delay overhead. ### Discussion

Regarding the success rate of scheduling, the summary of the comparative schemes in the aforementioned simulation experiment is presented in the following Table 8.

The scheduling scheme based on CFN-dyncast always selects the optimal CRN, which does not allow for load balancing among CRNs and may even exacerbate the problem of resource competition in distributed resource scheduling scenarios. The scheme based on Native DNS randomly selects the CRN, which, to some extent, balances the load and alleviates the resource competition issue. However, due to the limited flexibility of resource scheduling in the TCP/IP network, the resource competition problem cannot be further improved. The scheduling scheme based on Compute-Aware DNS makes a probabilistic selection of the CRN using the roulette-selection algorithm, which also allows for load balancing. However, it also faces the limitation of resource scheduling flexibility in the TCP/IP environment. The IPCRSA scheme proposed in this paper also uses the roulette-selection algorithm for the probabilistic selection of resource nodes, but its on-path computing resource scheduling mechanism in the ICN environment allows the computing resource request packets to adjust the scheduling targets in a timely manner based on the

Figure 11: Average scheduling delay in IPCRSA with different scheduling strategies.