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- W4383961566 abstract "Abstract: Cloud computing has revolutionized the way computational resources are provisioned and utilized. However, effective task scheduling in cloud environments remains a critical challenge due to the complexity of managing resources and meeting quality of service (QoS) requirements. In this paper, we address the multi-objective task scheduling problem by proposing a novel scheme that combines a hybrid algorithm, HGTSA, to achieve improved convergence rates and overall efficiency. Our main contributions include the development of a fitness function that considers makespan, system capacity, and resource utilization, as well as the implementation of the HGTSA algorithm. Through extensive simulations, we demonstrate the superiority of our proposed scheme, outperforming conventional genetic algorithms and achieving better makespan times. Furthermore, we highlight the future scope of research in leveraging machine learning techniques to further enhance task scheduling processes and optimize decision-making based on workload predictions and historical data." @default.
- W4383961566 created "2023-07-12" @default.
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- W4383961566 date "2023-07-31" @default.
- W4383961566 modified "2023-09-23" @default.
- W4383961566 title "Improved Task Scheduling for Cloud Computing Using Combination of Genetic Algorithm and Tabu Search Algorithm" @default.
- W4383961566 doi "https://doi.org/10.22214/ijraset.2023.54710" @default.
- W4383961566 hasPublicationYear "2023" @default.
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