# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
###### Abstract  
The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheuristics and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation. cloud computing; resource scheduling; metaheuristics; swarm intelligence; whale optimization algorithm; hybridization +
Footnote †: journal: Applied  
applications, along with state-of-the-art physical and virtualized hardware. At the current stage of technology advancements, cloud computing and the internet of things (IoT) have become essential concepts that set new goals for industry innovations [1]. One of the most important challenges in the cloud computing domain is resource scheduling. When performing resource scheduling, at least a satisfying level of quality of service (QoS) should be maintained by utilizing proper hardware infrastructure and algorithms. A component of the cloud infrastructure, usually referred as the broker in modern literature, is responsible for mapping requested end-users' tasks to the available virtualized hardware that is in most cases implemented in a form of virtual machines (VMs). The broker performs mapping by executing the scheduling algorithm. With the growth of the number of submitted tasks and the number of available resources, it becomes extremely difficult to map tasks to the appropriate VMs for execution. If an inappropriate scheduling algorithm is used, some VMs may be over-utilized or under-utilized, and the implication of such scenarios is performance degradation of the cloud system as a whole. The resource scheduling problem belongs to the group of NP (nondeterministic polynomial time) hard optimization problems.