## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies
By embedding resource requirements and preference parameters in the computing resource request packets, the SNs can dynamically adjust their scheduling destinations based on demand parameters and current resource status information during the packet routing process. This effectively alleviates resource contention issues in distributed scheduling scenarios, enhancing the overall scheduling success rate. Additionally, IPCRSA introduces preference-based computing resource scheduling strategies, where SNs execute corresponding resource scheduling strategies based on the Preference field in the packet header, catering to diverse user requirements. Finally, this paper presents a simulation implementation and performance analysis of IPCRSA. Experimental results demonstrate that IPCRSA has advantages in improving scheduling success rates. Moreover, as the scheduling delay constraints become loose, it exhibits even better scheduling performance. The preference-based computing resource scheduling strategies in IPCRSA achieve a balance between user preferences and resource scheduling load balancing, meeting diverse user requirements with minor overhead. Although IPCRSA shows significant advantages in scheduling performance, there are still some challenges that need to be addressed in future research. For instance, as a hierarchical scheduling architecture, if the root SN within an L\({}_{3}\) or L\({}_{2}\) level scheduling domain experiences faults or becomes disconnected, rapid fault detection and handling are critical issues. Typically, a viable solution involves monitoring faults and implementing corrective measures by periodically sending heartbeat signals between root and leaf SNs. Additionally, efficient aggregation of resource status information at the root SNs within L\({}_{3}\) and L\({}_{2}\) level scheduling domains is another pressing concern that requires attention. Furthermore, IPCRSA is based on an evolutionary ICN architecture that employs the SNRmethod. Given the significant differences between the SNR method and the NBR method, IPCRSA cannot be directly applied to ICN architectures that utilize the NBR method. However, the concept of integrating computing resource scheduling capabilities into the network itself, as well as the idea of conducting computing resource scheduling based on ICN, are equally applicable to ICN architectures based on the NBR method. Therefore, how to modify the IPCRSA under an NBR-based ICN architecture to enhance its broad applicability is also one of the issues worth exploring in the future. Conceptualization, Z.N., J.Y. and Y.L. ; methodology, Z.N., J.Y. and Y.L. ; software, Z.N. ; validation, Z.N. ; formal analysis, Z.N. ; investigation, Z.N. ; writing--original draft preparation, Z.N. ; writing--review and editing, J.Y. and Y.L. ; visualization, Z.N. ; supervision, J.Y. and Y.L. ; project administration, J.Y. and Y.L. ; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China: Application Demonstration of Polymorphic Network Environment for computing from the eastern areas to the western. (Project No. 2023 YFB2906404). Not applicable. We found here: [[https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)]([https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)) (accessed on 1 January 2018). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: