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DRAS is built on a novel, hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. A unique training strategy is presented to enable DRAS to rapidly learn the target environment.
Feb 11, 2021 · In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement ...
DRAS (Deep Reinforcement Agent for Scheduling) is built on a novel, hierarchical neural network incorporating special HPC scheduling features such as ...
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We propose a novel HPC scheduling agent named FARS (Failure-aware RL-based scheduler) by considering the effects of job failures.
We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy.
Nov 4, 2022 · The goal of the agent is two- fold: (1) to improve HPC scheduling performance beyond the existing approaches, and (2) to automatically adjust.
Oct 3, 2024 · Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available ...
We propose an improvement to the latest Deep Reinforcement Learning Agent for Scheduling (DRAS) model, called Improved Reinforcement Learning Scheduler (IRLS).
In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning.
The goal of the agent is twofold: ○. To improve HPC scheduling performance beyond the existing approaches. ○. To automatically adjust scheduling policies in ...