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Machine Learning-Based Online Scheduling in Distributed Computingстатья
- Авторы: Toporkov Victor, Yemelyanov Dmitry, Bulkhak Artem
- Сборник: Parallel Processing and Applied Mathematics
- Серия: Lecture Notes in Computer Science
- Том: 13827
- Год издания: 2023
- Издательство: Springer International Publishing AG
- Местоположение издательства: Cham, Switzerland
- Первая страница: 248
- Последняя страница: 259
- DOI: 10.1007/978-3-031-30445-3_21
- Аннотация: In this work, we propose and evaluate an online schedulerprototype based on machine learning algorithms. Online job-flow scheduler should make scheduling and resource allocation decisions for individual jobs without any prior knowledge of the subsequent job queue (i.e.,online). We simulate and generalize this task to a more formal 0–1 Knapsack problem with unknown utility functions of the knapsack items. Inthis way we evaluate the implemented machine learning-based solution toclassical combinatorial optimization algorithms. A hybrid machine learning and dynamic programming - based approach is proposed to considerand strictly satisfy the knapsack constraint on the total weight. As amain result the proposed hybrid solution showed efficiency comparableto the greedy knapsack approximation.
- Добавил в систему: Топорков Виктор Васильевич