## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & \begin{tabular}{c} **Distributed or** **Centralized** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Scheduling Strategy** \\ \end{tabular} & \begin{tabular}{c} **Discussions** \\ \end{tabular} \\ \hline \begin{tabular}{c} CFN- \\ dyncast \\ \end{tabular} & Distributed & TCP/IP & Select optimal CRN & \begin{tabular}{c} Unable to perform load balancing and \\ exacerbating resource competition \\ \end{tabular} \\ \hline \begin{tabular}{c} Native DNS \\ \end{tabular} & Distributed & TCP/IP & Select Randomly & \begin{tabular}{c} Load balancing was performed, but it was \\ unable to further alleviate resource contention \\ \end{tabular} \\ \hline \begin{tabular}{c} Compute- \\ Aware DNS \\ \end{tabular} & Distributed & TCP/IP & Probabilistic selection & \begin{tabular}{c} Load balancing was performed, but it was \\ unable to further alleviate resource contention \\ \end{tabular} \\ \hline IPCRSA & Distributed & ICN & \
\begin{tabular}{c} Probabilistic selection with \\ rescheduling (late-binding) \\ \end{tabular} &
\begin{tabular}{c} Load balancing was performed, and it can \\ further alleviate resource contention \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative schemes in the simulation experiment. current resource status information during the routing process, thereby greatly enhancing the flexibility of routing and scheduling. Therefore, it can further alleviate the resource competition problem while achieving load balancing, thus having an advantage in the success rate of scheduling compared with that of the previous three schemes. In terms of the average value of priority indicators, while the scheme of randomly selecting CRNs can effectively balance the load, it fails to accommodate user preferences. On the other hand, the IPCRSA's computing resource scheduling mechanism, based on user preferences, can balance the load among CRNs with minimal and negligible scheduling latency overhead while also taking into account user preferences, thus meeting diverse user requirements. Finally, a retrospective analysis of the simulation experiment is conducted. Although it successfully validated and analyzed the scheduling performance of the architecture discussed in this paper, demonstrating its superiority, there are still areas for improvement. Firstly, due to the scarcity of accessible open-source datasets of real-world computing resource clusters, the simulation experiment was based on a single dataset, which may lack generality due to its limited information. In future work, more comprehensive datasets could be employed to further validate the performance of the proposed architecture. Secondly, some modules within the current simulation environment, such as the hierarchical ICN name resolution system, were developed in previous works. While these modules fulfill basic name resolution functions, they may not match the performance of the latest standalone name resolution systems. Future efforts could focus on upgrading and improving these modules to align with the latest research findings. Lastly, the computing resource scheduling architecture discussed in this paper is based on an evolutionary ICN architecture, with the core aim of deploying the proposed scheduling methods in the real world at a lower cost. Future work could involve validating these methods in the real world to achieve a more \"realistic\" \"simulation environment\". ## 6 Conclusions

This paper proposes a novel ICN-based on-path computing resource scheduling architecture, IPCRSA, in the context of the Computing Network, primarily addressing the fundamental resource scheduling issues under service deployment requests. IPCRSA adopts a hierarchical structure.