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.