mlsanityData to 14 Oct 2026

An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing

arXiv 2406.01047 ↗First posted 3 Jun 2024cs.DC cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Efficient resource utilization and perfect user experience usually conflict with each other in cloud computing platforms. Great efforts have been invested in increasing resource utilization but trying not to affect users' experience for cloud computing platforms. In order to better utilize the remaining pieces of computing resources spread over the whole platform, deferrable jobs are provided with a discounted price to users. For this type of deferrable jobs, users are allowed to submit jobs that will run for a specific uninterrupted duration in a flexible range of time in the future with a great discount. With these deferrable jobs to be scheduled under the remaining capacity after deploying those on-demand jobs, it remains a challenge to achieve high resource utilization and meanwhile shorten the waiting time for users as much as possible in an online manner. In this paper, we propose an online deferrable job scheduling method called textitOnline Scheduling for DEferrable jobs in Cloud (OSDEC), where a deep reinforcement learning model is adopted to learn the scheduling policy, and several auxiliary tasks are utilized to provide better state representations and improve the performance of the model. With the integrated reinforcement learning framework, the proposed method can well plan the deployment schedule and achieve a short waiting time for users while maintaining a high resource utilization for the platform. The proposed method is validated on a public dataset and shows superior performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 27 references that are in the map. First posted on arXiv 3 Jun 2024.

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10 of 27 references.

References · 10

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  10. 2019Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learningcited by 11

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  2. 2026Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
  3. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  4. 2024Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
  5. 2024Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments
  6. 2025Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
  7. 2024Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments
  8. 2026On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach