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- W2981523391 abstract "Tasks scheduling in the cloud is one of the preliminary requirements to achieve optimized performance by the efficient allocation of tasks to resources. The cloud platform is vulnerable to the uncertainty in the task and resource parameters; hence, there is a need to develop self-learning models which handle uncertainty before taking scheduling decisions. In this paper, to handle the task uncertainty soft-set of tasks is created, and to handle the resource uncertainty soft-set based clustering mechanism is used. After handling the uncertainty, Double-Deep-Q learning (DDQ) scheduler is employed to draw optimal policies to efficiently map the soft-set of tasks to soft clusters of the resources. The expected value analysis method is used to analyze the performance of the DDQ scheduler in the finite and infinite cloud scenario. The performance of the DDQ scheduler compared to other well-known schedulers like PWL, PCS, PGA, and TPO is found to be good towards parameters such as execution time, accuracy, resource utilization ratio, and learning rate." @default.
- W2981523391 created "2019-11-01" @default.
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- W2981523391 date "2019-08-13" @default.
- W2981523391 modified "2023-09-23" @default.
- W2981523391 title "Soft-set based Double-Deep-Q Scheduler for Optimal Task Scheduling under Long-term Uncertainty in the Cloud" @default.
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