scholarly journals An Enriched Chaotic Quantum Whale Optimization Algorithm Based Job scheduling in Cloud Computing Environment

2019 ◽  
Vol 6 (4) ◽  
pp. 1753-1760
Author(s):  
Kiruthiga G ◽  
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Job Scheduling ◽  
Whale Optimization Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Whale Optimization

scholarly journals Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4893 ◽  
Author(s):  
Ivana Strumberger ◽  
Nebojsa Bacanin  ◽  
Milan Tuba ◽  
Eva Tuba
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
System Performance ◽  
Resource Scheduling ◽  
Whale Optimization Algorithm ◽  
Cloud System ◽  
Data Sets ◽  
Computing Paradigm ◽  
Whale Optimization

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.

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