## 3 Materials and Methods
### Proposed Algorithm
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameters** & **PSO** & **GA** & **ERES** & **FIMPSO** & **Proposed** \\ \hline \multicolumn{6}{c}{Montage} \\ \hline Makespan & 276 & 271 & 241 & 236 & 186 \\ Energy Consumption & 232.31 & 229 & 213.12 & 220.25 & 174 \\ Load Balance & 225 & 229 & 203 & 198 & 154 \\ \hline \multicolumn{6}{c}{Sight} \\ \hline Makespan & 5136 & 4987 & 4536.23 & 4592 & 3985 \\ Energy Consumption & 886 & 882 & 806.29 & 802 & 713 \\ Load Balance & 204 & 202 & 184 & 182 & 159 \\ \hline \multicolumn{6}{c}{LIGO} \\ \hline Makespan & 4852 & 4782 & 4232.36 & 4173 & 3374 \\ Energy Consumption & 1052 & 1023 & 966 & 936 & 788 \\ Load Balance & 236 & 227 & 202 & 198 & 155 \\ \hline \multicolumn{6}{c}{CyberShake} \\ \hline Makespan & 6431 & 6386 & 5631.72 & 5451 & 4523 \\ Energy Consumption & 188 & 185 & 161 & 157 & 134 \\ Load Balance & 246 & 241 & 218 & 214 & 176 \\ \hline \multicolumn{6}{c}{Epigenomics} \\ \hline Makespan & 70,836 & 70,332 & 40,257.17 & 39,788 & 32,902 \\ Energy Consumption & 15,732 & 15,655 & 13,849 & 13,725 & 10,736 \\ Load Balance & 253 & 248 & 227 & 223 & 176 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the proposed algorithm compared with those of standard and specialized schedulers on different workflows. Energy consumption is represented in KWh. Figure 2: Percent improvement gain of the proposed algorithm over the other methods on the Montage dataset. The proposed algorithm also achieved similar improvements compared to the other methods on the LIGO dataset. Figure 4 shows the results for the LIGO dataset. The results show that the proposed algorithm achieved percent improvement gains of 30.46, 29.44, 20.28, and 19.14 in terms of makespan over the other methods. For the same dataset, the improvement gains in terms of energy consumption over the compared methods were 25.09, 22.97, 18.45, and 15.81. In terms of load balance, the improvement gains of the proposed algorithm over PSO, GA, ERES, and FIMPO were 34.32, 31.71, 32.26, and 21.71, respectively. Figure 5 shows the results of the percent improvement gain of the proposed algorithm over the other algorithms for the CyberShake dataset. The improvement gains in terms of the makespan over the other methods for this dataset were 29.66, 29.17, 19.68, and 17.02. The improvement gains over PSO, GA, ERES, and FIMPO in terms of energy consumption were 28.72, 27.56, 16.77, and 14.64, respectively. For the same dataset, the improvement gains in terms of load balance were 28.45, 26.97, 19.26, and 17.75 over PSO, GA, ERES, and FIMPO, respectively. Figure 4: Percent improvement gain of the proposed algorithm over the other methods on the LIGO dataset. Figure 3: Percent improvement gain of the proposed algorithm over the other methods on the Sight dataset. In the case of the Epigenomics datasets, the proposed algorithm achieved percent improvement gains of 53.55, 53.21, 18.27, and 17.30 over the other algorithms. In terms of the energy consumption, the proposed algorithm achieved percent improvement gains of 28.72, 27.56, 16.77, and 14.64 over PSO, GA, ERES, and FIMPSO, respectively. For the same dataset, the improvement gains of the proposed algorithm over the other methods in terms of load balance were 28.45, 26.97, 19.26, and 17.75. Figure 6 shows the detailed results. ## 5 Conclusions

Cloud computing is a technology that is widely used in many domains, including scientific applications. Due to the large numbers of users and applications, cloud computing environments suffer from some issues. These issues include energy consumption, fault tolerance, user deadlines, etc. Scheduling and VM placement are techniques that are used to handle these issues. There are many solutions for scheduling cloud resources. This article addresses the problem of energy efficiency and resource utilization in cloud data centers. The proposed work is based on task classification, thresholds, and queueing. In the first phase, workflow tasks are placed in queues according to the number of levels and the execution times of the tasks.