This paper explores the application of evolutionary algorithms in addressing the challenges of dynamic resource allocation within the context of cloud computing. With the rapid expansion of cloud services and the increase in demand, efficient resource management is critical to ensure optimal performance and cost-effectiveness. Traditional methods of resource allocation often struggle to adapt to the dynamic nature of cloud environments, resulting in inefficient resource utilization and service degradation. By employing evolutionary algorithms, this study proposes a novel approach to dynamically allocate resources in a cloud computing setting. The algorithms, inspired by the principles of natural selection and genetic inheritance, are designed to optimize resource allocation by evaluating and adapting to the changing workload and resource requirements. The effectiveness of this approach is demonstrated through simulations, showing significant improvements in resource utilization and service quality compared to conventional methods. The findings of this study contribute to the advancement of resource management strategies in cloud computing and provide a foundation for future research in this field.
White, O. Evolutionary Algorithms for Dynamic Resource Allocation in Cloud Computing. Transactions on Applied Soft Computing, 2021, 3, 21. https://doi.org/10.69610/j.tasc.20210821
AMA Style
White O. Evolutionary Algorithms for Dynamic Resource Allocation in Cloud Computing. Transactions on Applied Soft Computing; 2021, 3(2):21. https://doi.org/10.69610/j.tasc.20210821
Chicago/Turabian Style
White, Olivia 2021. "Evolutionary Algorithms for Dynamic Resource Allocation in Cloud Computing" Transactions on Applied Soft Computing 3, no.2:21. https://doi.org/10.69610/j.tasc.20210821
APA style
White, O. (2021). Evolutionary Algorithms for Dynamic Resource Allocation in Cloud Computing. Transactions on Applied Soft Computing, 3(2), 21. https://doi.org/10.69610/j.tasc.20210821
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