## 4 Example and Comparison Results
### The Task Dispatching Phase
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Node A** & **Node B** & \(\mathit{SV}\) & \(\mathit{MSV}\) \\ \hline Task \(a\) & 22345 & 23526 & 1181 & - \\ Task \(b\) & 16667 & 17980 & 1263 & - \\ Task \(c\) & 31083 & 31897 & 814 & - \\ Task \(d\) & 24712 & 25156 & 444 & - \\ Task \(e\) & 17018 & 18069 & 1081 & - \\ Task \(f\) & 12050 & 13289 & 1239 & - \\ Task \(g\) & 19035 & 21486 & 2451 & 21486 \\ Task \(h\) & 13911 & 14602 & 697 & - \\ Task \(i\) & 8016 & 8536 & 520 & - \\ Task \(j\) & 13618 & 14596 & 978 & - \\ Task \(k\) & 27861 & 25156 & 295 & - \\ Task \(l\) & 43336 & 44731 & 1395 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the average \(\mathit{ECT}\) values of tasks in Node A under Case 2.  
average value of makespan is taken, as shown in Figures 2-5. In Figure 2, the proposed algorithm has a better MakeSpan than the others, especially in the large tasks environment. In addition, the load balancing index can be defined by \(r_{min}/r_{max}\), where \(r_{min}\) is the shortest completed task time of all tasks and \(r_{max}\) is the longest completed task time of all tasks [1; 15]. In general, the value of the load balancing index is a number between 0 and 1, with 0 being the worst load balance and 1 being the optimal load balance. As shown in Figures 3-5, the ACSS algorithm can obtain the best load balancing index (0.88) over Sufferage (0.87) and MaxSufferage (0.83). Because the ACSS algorithm uses the distribution of the average value, the MakeSpan of each node can achieve similar results. However, MaxSufferage completed time is better than that of Sufferage, but the load balancing results are similar. This is because MaxSufferage did not consider the load status of the node during the selection of tasks. As a result, the proposed ACSS algorithm can obtain the best results of complete time and load balance among these algorithms even if the heterogeneous cloud computing network is complex. Figure 3: The results of load balancing index in the Sufferage scheduling algorithm for (**a**) \(n=4\) and number of task = 50 and (**b**) \(n=4\) and number of tasks = 100. Figure 2: The comparison results of MakeSpan among Sufferage, MaxSufferage, and ACSS algorithms for (**a**) \(n=4\) and number of tasks = 50 and (**b**) \(n=4\) and number of tasks = 100. Besides this, the formula \(RU=\frac{\sum_{j=1}^{N}Tc_{j}}{Nm}\times 100\%\) is used to calculate the ratio of resource utilization to show whether the use of resource in this paper is maximized. In factor \(RU\), the \(TC_{j}\) represents the total expected completion time by virtual machine \(j\), \(N\) represents the number of virtual machines, and \(m\) represents the final completion time of the virtual machine. The related ratio results of resource utilization are shown in Figure 6. In Figure 6, the ratio of resource utilization of ACCS can reach \(89\%\), and this result is better than for other algorithms. This is because that the average value is used to consider the allocation status of nodes in the ACSS algorithm  
Figure 4: The results of load balancing index in the MaxSufferage scheduling algorithm for (**a**) \(n=4\) and number of tasks = 50 and (**b**) \(n=4\) and number of tasks = 100. Figure 5: The results of load balancing index in the Advanced Cluster Sufferage Scheduling (ACSS) scheduling algorithm for (**a**) \(n=4\) and number of tasks = 50 and (**b**) \(n=4\) and number of tasks = 100. Figure 6: The ratio of Resource Utilization in the ACSS scheduling algorithm for (**a**) \(n=4\) and number of tasks = 50 and (**b**) \(n=4\) and number of tasks = 100. Subsequently, the parameter of matching proximity [11] is used to evaluate the degree of proximity of various scheduling algorithms. In Figure 7, the MET (Minimum Execution Time) and ECT (Expected Computing Time) are used to estimate whether the task can be quickly matched.