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- W4285162415 abstract "In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0–1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm." @default.
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- W4285162415 date "2022-01-01" @default.
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- W4285162415 title "Machine Learning-Based Scheduling and Resources Allocation in Distributed Computing" @default.
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- W4285162415 doi "https://doi.org/10.1007/978-3-031-08760-8_1" @default.
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