## 4 Simulation Results
### Quantitative Analysis  
The experimental results of different optimization algorithms by means of execution time and cost are presented in Table 2. In addition to this, the performances of the optimization algorithms are analyzed on different platforms such as Storm, Flink, Spark, and Kafka. By inspecting Table 2, the SCEHO-IPSO algorithm is seen to obtain a minimum execution time of 612 milliseconds (ms) and a cost of 62 on the Kafka platform, which are superior compared to other optimization algorithms (ALO, GA, ACO, PSO, GWO, MVO, and EHO) and other platforms (Storm, Flink, and Spark). The two common parameters assumed in the comparative optimization algorithms are a population size of 100 and a maximum iteration of 100. The specific parameters of ALO are as follows: the number of dimensions is five, the lower bound is 0.1, and the upper bound is 0.8. The assumed parameters of GA are as follows: the mutation probability is 0.02, the crossover probability is 0.60, and the number of demes is six. The parameters of ACO are as follows: the time factor is two, the saving matrix factor is two, the visibility coefficient is three, and the pheromone concentration coefficient is one. The maximum initial velocity is 15, the minimal initial velocity is five, the alpha is 0.8, and the beta is 0.8; these are the specific parameters considered in the PSO algorithm. The assumed parameters of the GWO and MVO algorithms are as follows: the number of appliances is 12, the coefficient vector is one, the TDR is one, and the WEP is 0.2. The parameters of EHO are as follows: the alpha is 0.5, the beta is one, the upper bound is 0.9, the number of clans is 10, the set elitism is two, and the lower bound is 0.3. Generally, the highly available fault-tolerant task scheduling helps in improving the business goals. The Kafka platform includes advantages such as high enterprise security, real time analysis,  
Correspondingly, the experimental results of different optimization algorithms in light of makespan and memory storage are specified in Table 4. As seen in Table 4, the proposed SCEHO-IPSO algorithm has minimum makespan values of 88, 73, 45, and 44 on the platforms Storm, Flink, Spark, and Kafka. On the other hand, the proposed SCEHO-IPSO algorithm has a consumed minimal memory storage of 338 kb, 336 kb, 322 kb, and 309 kb on the platforms Storm, Flink, Spark, and Kafka. The achieved results are superior when compared to traditional optimization algorithms such as ALO, GA, ACO, PSO, GWO, MVO, and EHO. Visual comparisons of different optimization algorithms in terms of makespan and memory storage are specified in Figures 7 and 8. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Makespan**} \\ \hline
**Platform** & **ALO** & **GA** & **ACO** & **PSO** & **GWO** & **MVO** & **EHO** & **SCEHO-IPSO** \\ \hline Storm & 288 & 283 & 193 & 188 & 190 & 144 & 94 & 88 \\ Flink & 276 & 279 & 187 & 176 & 158 & 142 & 90 & 73 \\ Spark & 244 & 232 & 143 & 165 & 148 & 122 & 88 & 45 \\ Kafka & 240 & 212 & 123 & 142 & 122 & 110 & 78 & 44 \\ \hline \multicolumn{10}{c}{Memory storage (kb)} \\ \hline Platform & ALO & GA & ACO & PSO & GWO & MVO & EHO & SCEHO-IPSO \\ \hline Storm & 512 & 538 & 727 & 721 & 573 & 698 & 413 & 338 \\ Flink & 532 & 521 & 632 & 658 & 549 & 690 & 479 & 336 \\ Spark & 454 & 477 & 553 & 630 & 490 & 532 & 493 & 322 \\ Kafka & 380 & 392 & 442 & 532 & 422 & 504 & 379 & 309 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different optimization algorithms in light of makespan and memory storage. Figure 6: Visual comparison of different optimization algorithms in terms of latency.