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 metaheurisitcs 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.
Highlights
One of the most important benefits of cloud computing is on-demand provisioning of requested services and resources over high speed computer networks
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 whale optimization algorithm (WOA) approach by performing hybridization with other swarm algorithms that proved to be efficient optimization methods?”
In order to more adequately evaluate the performance of the proposed metaheuristics, we have considered the degree of imbalance (DI) which can be calculated as: DI =
Summary
One of the most important benefits of cloud computing is on-demand provisioning of requested services and resources over high speed computer networks. 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. 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?”. All the necessary details and inner workings of the proposed research, including the settings of the algorithms’ control parameters, simulation framework settings and utilized data sets, are fully provided in this paper, the researchers who want to implement proposed approaches and to run simulations have more than enough information to do this on their own
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