# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 6 Conclusion and Future Research  
In this paper an implementation of the improved WOA swarm intelligence metaheuristics adapted for tackling the resource scheduling challenge in cloud computing environments was proposed. The original WAO approach was enhanced by using hybridization as a promising technique that is widely adopted for improving swarm algorithms. By using hybridization, the best components of two or more algorithms are combined.  
The main objective of the research proposed in this paper was to improve the resource scheduling problem in cloud computing environments by using swarm intelligence algorithms. The secondary objective of the proposed research was to improve WOA by hybridization with other state-of-the-art swarm algorithms to address the observed deficiencies of the original WOA implementation.  
The proposed hybrid algorithm, WOA-AEFS addresses deficiencies of inappropriate balance adjustments between exploitation and exploration and the premature convergence of the original WOA version by adapting exploration mechanism from the ABC algorithm and by incorporating exploitation search procedure from the FA metaheuristic.  
In the presented research, a simulation in the standard environment with classic benchmark instances was utilized as the research methodology. Resource scheduling simulations were conducted in the CloudSim framework environment.  
To establish and validate performance of the proposed WOA-AEFS approach, three types of tests (simulations) have been performed. The devised approach was first tested on a standard set of bound-constrained benchmarks and the obtained solution quality and the convergence speed were compared with the results generated by the original WOA implementation and PSO, GSA, DE, and FEP metaheuristics, that were all tested on the same benchmark instances.  
The last two simulations were performed by using two cloud computing resource scheduling models. In simulations with the first resource scheduling model (single-objective model with MS objective), real data set obtained from NASA iPSC super computing environment was employed and performance of the WOA-AEFS with the original WOA has been compared, as well as with the PSO and the MSDE state-of-the-art metaheuristics.  
The second resource scheduling model falls into the category of multi-objective optimization, where the MS and budget costs objectives with task performance and task deadline constraints were considered. In these tests, the WOA-AEFS generated solution quality has been compared with the original WOA, CPSO, and PBACO metaheuristics, as well as with FCFS and Min-Min heuristics.  
In all conducted simulations (bound-constrained benchmarks and cloud computing resource scheduling), the WOA-AEFS showed substantially better performance than all approaches included in the comparative analysis. In this way, a hybrid algorithm was devised that overcomes deficiencies of the original WOA metaheuristic, and improvements in solving resource scheduling problem in cloud computing has also been established.  
Future research could implement and validate other swarm intelligence algorithms in original, as well as in modified/hybridized versions to other challenges and problems in cloud computing, such as load balancing, migration and VMs provisioning, energy consumption reduction, etc. It would also be interesting to see how efficiently the proposed WOA-AEFS is able to tackle large-scale global optimization challenges.  
[PERSON] and [PERSON] proposed the idea. [PERSON], [PERSON] and [PERSON] have implemented and adapted algorithms. The entire research project was conceived and supervised by [PERSON] The original draft was written by [PERSON], [PERSON] and [PERSON]; review and editing was performed by [PERSON] All authors participated in the conducted experiments and in discussion of the experimental results.  
**Funding:** This research is supported by the Ministry of Education and Science of Republic of Serbia, Grant No. III-44006.  
**Conflicts of Interest:** The authors declare no conflict of interest.