# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
### Objectives, Contributions, Question and Methodology of the Proposed Research  
In this paper we propose an implementation of the enhanced whale optimization algorithm (WOA) adapted for tackling the resource scheduling challenge in cloud computing environments. We improved the basic WOA approach by performing hybridization with the artificial bee colony (ABC) and firefly algorithms (FA), which are also categorized as swarm intelligence metaheuristics. The WOA was created by [PERSON] and [PERSON] in 2016 [22] and shortly after many modified and improved WOA versions emerged [23; 24; 25]. Some WOA adaptations for resource scheduling in cloud computing can be found in the literature survey [26]. However, a hybrid between the WOA and ABC and FA metaheuristics, as is proposed in this paper, has never been implemented and tested before on any problem. The main motivation behind the proposed research is the fact that swarm intelligence metaheuristics proved to be robust and effective optimization methods for solving many different types of real-environment NP hard optimization problems [27; 28], as well as the fact that swarm intelligence algorithms have already been successfully applied to various models (single and multi-objective) of resource scheduling in cloud computing [29; 30; 2]. The main objective of the research proposed in this paper is to try to establish further enhancements in tackling the resource scheduling challenge in cloud computing environments by using swarm intelligence approaches. Guided by the main objective, we have implemented a hybridized WOA metaheuristic and have validated its performance for two resource scheduling models. The first model is single-objective, and aims to minimize the makespan indicator, while the second model belongs to the group of multi-objective optimization, where makespan and total cost objectives were taken into consideration. The secondary objective of the proposed research is an attempt to address observed deficiencies of the original WOA implementation by performing hybridization with other state-of-the-art swarm intelligence algorithms. For this purpose, as well as for the goal of establishing more accurate evaluation of the proposed hybrid WOA's performance, we first tested our devised approach on a larger set of bound-constrained benchmarks and compared the obtained solutions' quality and convergence speed with the results of the original WOA implementation against the same test instances. It has to be noted that the authors have conducted research with improved and hybridized swarm intelligence algorithms before [31; 32], and that they have also implemented some swarm intelligence approaches for resource scheduling tasks in cloud computing environment [4; 33]. Hence, the research presented in this paper is the result of authors previous experience in this domain, as well as their recent work with WOA metaheuristic and resource scheduling problems in cloud computing. According to the research objectives, the basic research question addressed in this paper is: \"Is it achievable to establish further improvements in solving the cloud computing resource scheduling problem by using swarm intelligence algorithms?\" The second research question, that falls into domain of bio-inspired metaheuristics, can be formulated as follows: \"Is it possible to improve performance of original WOA approach by performing hybridization with other swarm algorithms that proved to be efficient optimization methods?\"  
In the presented research, a simulation in the standard environment with classic benchmark instances was utilized as the research methodology. Three types of simulations were performed. The first simulation was conducted for standard unconstrained benchmark problems, while the second and the third were performed by using two different resource scheduling models in cloud computing with real (second simulation), as well as with artificial (third simulation) data sets.