# Balancer Genetic Algorithm--A Novel Task Scheduling Optimization Approach in Cloud Computing
#### 3.2.6 Bancer Operator
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**VM No.** & **Power (MIPS)** & **VM No.** & **Power (MIPS)** \\ \hline
1–7 & 100 & 27-32 & 1250 \\ \hline
8–14 & 500 & 33-38 & 1500 \\ \hline
15–20 & 750 & 39-44 & 1750 \\ \hline
21–26 & 1000 & 45-50 & 4000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Power of VMs in experimentation. \begin{table}
\begin{tabular}{l l} \hline \hline
**Techniques** & **Performance Metrics Demonstrated** \\ \hline MGGS [13] & Load balancing and makespan \\ \hline ETA-GA [18] & Makespan and network failure \\ \hline RALBA [3] & Load balancing, throughput and makespan \\ \hline DSOS [24] & Load balancing, makespan, response time and convergence \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Benchmark Techniques. ### Discussion on the Comparison

Figure 3 shows the makespan values on the left--skewed dataset. In almost all batch sizes BGA performs better. However, MGGS and RALBA are close competitors. On average, overall, BGA achieved 27.3, 71.9, 40.5 and 4.6% improvement in makespan compared with MGGS, ETA-GA, DSOS and RALBA, respectively. For a small batch size of 100, RALBA slightly surpasses BGA in makespan. Still, for large batch sizes, which are more realistic for cloud systems, BGA outperforms all the benchmark techniques. Figure 4 shows the results of makespan on a right--skewed dataset. BGA has achieved 19.8, 72.2, 42.4 and 3.2% average improvement in makespan compared with MGGS, ETA-GA, DSOS and RALBA, respectively. Similarly, Figure 5 shows makespan value on a normal dataset. BGA has attained better makespan with average improvement of 23.3, 72.1, 41.6 and 5.9% as compared to

Figure 4: Makespan results on the right—skewed dataset. Figure 3: Makespan results on the left—skewed dataset. MGGS, ETA-GA, DSOS and RALBA, respectively. Finally, improvement of BGA on the uniform dataset is reflected in Figure 6. BGA performed 19.2, 71.9, 42.1 and 6.7% better in makespan than MGGS, ETA-GA, DSOS and RALBA, respectively. It is evident that, overall, on all categories and batch sizes of datasets, BGA outperforms. Specifically for the uniform dataset, RALBA was a close competitor and deteriorated to highest level of 6.7%. Even if MGGS is close at some points, MGGS is more computationally expensive than other meta-heuristics compared here, so it is not a right choice to consider, especially where time is concerned. There are different ways to measure load balancing, and ARUR is a very popular one of them. The value of ARUR is between 0 and 1, but it can signify the percent of workload balance. BGA is a load balancing technique. Therefore, this QoS parameter is very important for the claim of better load balancing. On left--skewed dataset the load

Figure 5: Makespan results on the normal dataset. Figure 6: Makespan results on the uniform dataset. balancing achieved is shown in Figure 7. At batch size 500, MGGS performs better; for a small batch size of 100, RALBA performs better. The average improvement of BGA over MGGS, ETA-GA, DSOS and RALBA in terms of load balancing is 5.2, 77, 60.6 and 5.4%, respectively. MGGS, DSOS and RALBA are the techniques that have reported better load balancing. Out of these, only RALBA and MGGS have deployed a load balancing mechanism in their approach. Here again, just like makespan, RALBA is a close competitor of BGA in load balancing. However, the results show that, on average, 5.4% better load balancing is achieved on left--skewed and likewise other datasets. On the right--skewed dataset the improvement in ARUR is shown in Figure 8. BGA has attained 12.5, 89.5, 70.1 and 6.1% better ARUR than MGGS, ETA-GA, DSOS and RALBA, respectively. At a batch size of 400, MGGS performs better, but generally it does not perform better than BGA. Figure 9 shows results of ARUR on a normal dataset. Here, BGA has achieved 12.5, 82.2, 65.2 and 6% better load balancing as compared to MGGS, ETA-GA, DSOS and RALBA, respectively.