## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies  
The computing resource scheduling in IPCRSA is preference-based, with different user preferences corresponding to distinct computing resource scheduling strategies. The execution of the preference-based computing resource scheduling strategies involves two stages: The first stage evaluates various CRNs using a node-scoring algorithm corresponding to the user preferences based on the status information of each CRN in the local resource status information table. The second stage involves selecting the destination CRN based on the scoring results. In IPCRSA, Users with varying resource request preferences are most concerned about different status parameters of computing resource nodes, which are defined in this paper as priority indicators corresponding to each preference. Currently, IPCRSA has designed specific node-scoring algorithms for various resource request preferences and preference combinations, as outlined in Table 5, with the input parameters being the priority indicators corresponding to them. When a User has no specific preferences, a node-scoring algorithm corresponding to the default preference, i.e., the load-first preference, is employed to score each CRN. Specifically, the SN calculates the load score for each CRN using Equations (1)-(4). Here, \begin{table}
\begin{tabular}{l l l} \hline \hline
**RRP Type** & **Type Name** & **Input Parameters (Priority Indicators)** \\ \hline RRP\({}_{0}\) (Default) & Load-First & CPU\_Usage/Memory\_Usage/Storage\_Usage/ \\  & Bandwidth\_Usage \\ \hline RRP\({}_{1}\) & CPU-First & Available\_CPU\_Cores \\ RRP\({}_{2}\) & Memory-First & Available\_Memory \\ RRP\({}_{3}\) & Storage-First & Available\_Storage \\ RRP\({}_{4}\) & Bandwidth-First & Available\_Bandwidth \\ RRP\({}_{5}\) & Cost-First & Price \\ RRP\({}_{6}\) & Efficiency-First & PUE \\ RRP\({}_{7}\) & Delay-First & Communication\_Delay \\ \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resource request preferences in IPCRSA. \(u_{i_{c}}\), \(u_{i_{in}}\), \(u_{i_{s}}\), and \(u_{i_{b}}\) represent the normalized utilization rates of CPU, memory, storage, and bandwidth, respectively, for the \(i\)th (\(i\in N\)) CRN. \(N\) represents the set of CRNs' ordinal numbers. These parameters correspond to CPU_Usage, Memory_Usage, Storage_Usage, and Bandwidth_Usage in the local resource status information table. \(\bar{u}_{i}\) is the mean of the normalized utilization rates for these four basic type resources. \(MaxNodeScore\) is the predefined maximum score for CRNs, typically set to 10 or 100. \(s_{i}(i\in N)\) represents the final score for the CRN. The higher the score, the lower the load on the CRN. \[\bar{u_{i}}=\frac{\left(u_{i_{c}}+u_{i_{in}}+u_{i_{i_{s}}}+u_{i_{b}}\right)}{4} \tag{1}\]  
\[s_{i_{1}}=(1-\sqrt{\frac{\left(u_{i_{c}}-\bar{u}_{i}\right)^{2}+\left(u_{i_{in }}-\bar{u}_{i}\right)^{2}+\left(u_{i_{s}}-\bar{u}_{i}\right)^{2}+\left(u_{i_{ b}}-\bar{u}_{i}\right)^{2}}{4}})\times MaxNodeScore \tag{2}\]  
\[s_{i_{2}}=\frac{\left(1-u_{i_{c}}\right)+\left(1-u_{i_{in}}\right)+\left(1-u_ {i_{s}}\right)+\left(1-u_{i_{b}}\right)}{4}\times MaxNodeScore \tag{3}\]  
\[s_{i}=\frac{s_{i_{1}}+s_{i_{2}}}{2},\ i\in N \tag{4}\]  
When Users have preferences in terms of the resource itself, cost, energy consumption, and scheduling delay, i.e., having one or more preferences from RRP\({}_{1}\) to RRP\({}_{7}\), the SN scores each CRN based on the TOPSIS comprehensive evaluation method. Firstly, an original data matrix \(X_{n\times m}\) is constructed based on \(n\) CRNs and \(m\) input parameters corresponding to user preferences in the local resource status information table. The row vector represents the value vector of \(m\) input parameters for the \(i\)th (\(i\in N\)) CRN in this scoring algorithm. The column vector represents the values of the \(n\) CRNs on the \(j\)th (\(j\in M\)) input parameter. \(M\) represents the set of input parameters' ordinal numbers. \[X_{n\times m}=