# An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
## 7 Conclusions  
In this paper, an enhanced discrete variant of the metaheuristic symbiotic organism search algorithm was designed and implemented. The discrete variant of this strategy was inspired by the symbiotic interactions displayed by organisms in an ecosystem. Hence, the strategy imitates the different types of symbiotic interactions (mutualism, commensalism, and parasitism) in order to improve the quality of a particular objective function. The proposed strategy was simulated with the CloudSim toolkit to schedule independent tasks. The makespans and response times of VMs were measured, and eDSOS was \begin{table}
\begin{tabular}{l l l l l l} \hline
**Task Size** & **DSOS** & **eDSOS** & **Improvement (\%)** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline
100 & 15.39 & 9.48 & 62.34 & 4.3641 & 5.32 \(\times\) 10\({}^{-5}\) \\
200 & 28.54 & 21.15 & 34.94 & 4.2642 & 7.49 \(\times\) 10\({}^{-5}\) \\
300 & 40.40 & 35.61 & 13.45 & 1.9637 & 0.0544 \\
400 & 54.68 & 48.62 & 12.46 & 2.0785 & 0.0421 \\
500 & 76.88 & 66.47 & 15.66 & 3.2315 & 0.0020 \\
600 & 90.54 & 76.43 & 18.46 & 4.0290 & 0.0002 \\
700 & 99.93 & 91.28 & 9.48 & 1.4371 & 0.1561 \\
800 & 123.01 & 108.87 & 12.99 & 0.9288 & 0.3568 \\
900 & 129.10 & 121.30 & 6.43 & \(-\)0.5184 & 0.6062 \\
1000 & 145.81 & 132.59 & 9.97 & 0.6189 & 0.5384 \\ \hline \end{tabular}
\end{table} 
Table 10: Comparison of the response times of DSOS and eDSOS for the dataset with the left-half distribution. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Task Size** & **DSOS** & **eDSOS** & **Improvement (\%)** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline
100 & 9.83 & 6.47 & 51.93 & 3.3613 & 0.0014 \\
200 & 19.71 & 14.68 & 34.26 & 3.8736 & 0.0003 \\
300 & 29.92 & 25.27 & 18.40 & 3.6055 & 0.0007 \\
400 & 37.21 & 36.87 & 0.92 & 0.1791 & 0.8585 \\
500 & 49.30 & 44.14 & 11.69 & 2.4222 & 0.0186 \\
600 & 58.33 & 56.93 & 2.46 & 0.6615 & 0.5109 \\
700 & 70.99 & 65.15 & 8.96 & 2.2098 & 0.0311 \\
800 & 82.20 & 75.22 & 9.28 & \(-\)1.0545 & 0.2960 \\
900 & 95.87 & 84.25 & 13.79 & 0.5383 & 0.5925 \\
1000 & 99.02 & 90.76 & 9.10 & 1.3714 & 0.1755 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of the response times of DSOS and eDSOS for the dataset with the right-half distribution. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Task Size** & **DSOS** & **eDSOS** & **Improvement (\%)** & \(t\)**-Test Value** & \(p\)**-Value** \\ \hline
100 & 12.22 & 9.14 & 33.70 & 3.355 & 0.0014 \\
200 & 23.16 & 19.90 & 16.38 & 2.071 & 0.0428 \\
300 & 40.59 & 31.22 & 30.01 & 4.189 & \(9.7\times\) 10\({}^{-5}\) \\
400 & 49.02 & 45.35 & 8.09 & 2.079 & 0.0421 \\
500 & 63.39 & 56.59 & 12.02 & 2.818 & 0.0066 \\
600 & 74.11 & 69.72 & 6.30 & 1.594 & 0.1165 \\
700 & 87.29 & 80.95 & 7.83 & 2.135 & 0.0370 \\
800 & 99.64 & 91.51 & 8.88 & 0.732 & 0.4670 \\
900 & 117.27 & 103.70 & 13.09 & 0.821 & 0.4150 \\
1000 & 128.05 & 115.48 & 10.89 & 0.683 & 0.4975 \\ \hline \end{tabular}
\end{table} 
Table 12: Comparison of the response times of DSOS and eDSOS for the uniformly distributed dataset. shown to be better than the benchmark, DSOS. The improvement of the performance of eDSOS compared to DSOS increased in most instances when the search space expanded. The best improvements in terms of the minimisation of the makespan with eDSOS were 13.68% to 20.05%, 14.68% to 18.37%, 11.58% to 26.23%, and 8.16% to 18.90% less than those with DSOS for the normal, left-half, right-half, and uniform distributions, respectively. Similarly, the best improvements in terms of the minimisation of the response time with eDSOS were 02.23% to 45.08%, 06.43% to 62.34%, 0.92% to 51.93% and 06.30% to 33.70% less than those with DSOS for the normal, left-half, right-half, and uniform distributions, respectively. Thus, the results of the two-sample \(t\)-test showed that eDSOS clearly outperformed DSOS through almost the entire process, and did so more glaringly in the later state of the search process.