# Balancer Genetic Algorithm--A Novel Task Scheduling Optimization Approach in Cloud Computing
#### 3.2.6 Bancer Operator
Similarly, on the uniform dataset BGA again performs better than MGGS, ETA-GA, DSOS and RALBA with the improvement of 12.5, 82.2, 65.2 and 6%, respectively, as expressed in Figure 10. Load balancing of BGA is highest as compared to RALBA, a close competitor on the right--skewed dataset. Generally, it is observed that for batch sizes of 400 and 500, at some places MGGS performs better, but it has the overhead of too much time for load balancing. Overall, BGA outperforms in load balancing than all other techniques. Figure 8: ARUR results on the right—skewed dataset. Figure 4:0 ARUR results on the normal dataset. Throughput is the number of tasks computed in a unit time, so it depends on the makespan value. Thus, when makespan is good, it is evident that throughput would also be good. According to the claim of this research, BGA has better throughput than MGGS, ETA-GA, DSOS and RALBA. Figures 11-14 show the throughput of BGA and other techniques on left--skewed, right--skewed, normal and uniform datasets, respectively. On the left--skewed dataset the improvement is 29.7, 64, 40 and 4.6% over MGGS, ETA-GA, DSOS and RALBA, respectively. On the right--skewed dataset 24.1, 65.4, 43 and 4% improvement in throughput is achieved as compared to MGGS, ETA-GA, DSOS and RALBA, respectively. On the normal dataset 26.1, 64.4, 41.7 and 5.1% improvement is achieved over MGGS, ETA-GA, DSOS and RALBA, respectively. On the uniform dataset 23.4, 65.5, 42.9 and 6.9% improvement is achieved over MGGS, ETA-GA, DSOS and RALBA, respectively. Figure 4:1 Average throughput results on the left—skewed dataset. Figure 4:2 ARUR results on the uniform dataset. Figure 4:3 Average throughput results on the uniform dataset. Figure 4:4 Average throughput results on the right—skewed dataset. Figure 4:5 Average throughput results on the normal dataset. ## 6 Conclusions

In this research, state-of-the-art heuristic and meta-heuristic task schedulers are analyzed in depth, and it is determined that scheduling results in inefficient load balancing of cloud resources. The need for multi-objective optimization is expressed as a motivation, where load balancing serves as a significant metric parallel to makespan for efficient task scheduling. For exploration of a large search space of possible solutions, meta-heuristics are opted for, and when heuristics are merged, an additional capability to explore more optimal solutions is complemented. In this work, a novel task scheduling technique is presented, namely Balancer Genetic Algorithm (BGA), to improve makespan and load balancing. A load balancing mechanism is fused in GA with other necessary tuning of parameters to achieve better results. Formulations of multi-objective fitness functions are expressed in detail. CloudSim is used as a platform for simulating the behavior of presented techniques in a cloud data center, where resources and tasks to be mapped on resources are heterogeneous. Rigorous experiments are performed to demonstrate that BGA outperforms state-of-the-art MGGS, ETA-GA, DSOS and RALBA schedulers for performance measures of makespan, throughput and load balancing. Heterogeneous distribution of workload with varying batches of tasks is used to avoid biased dataset-dependent experimentation. Currently, BGA does not cater to Service Level Agreements to prioritize tasks having dependency. In the future, BGA can be extended to incorporate Service Level Agreements where tasks have unequal priorities. Conceptualisation: R.G., A.B.S., N.A. and N.R. ; Methodology: R.G., A.B.S., N.A. and A.A.A. ; Software: R.G. ; Validation: A.B.S., N.A. and T.A. ; Formal analysis: A.B.S., N.A. and N.R. ; Resources: N.A., A.A.A., T.A. and N.R. ; Initial draft: R.G. and A.B.S. ; Review and editing: N.A. and A.B.S. ; Supervision: A.B.S. ; Project administration: N.A. and N.R. ; Funding acquisition: A.A.A., T.A. and N.R. All authors have read and agreed to the published version of the manuscript.