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Existing cloud computing container scheduling methods have significant deficiencies in resource utilization and performance. To address these issues and improve resource efficiency and cluster performance, we propose a heterogeneous multi-container scheduling mechanism based on Deep Reinforcement Learning (DRL). This technology employs an innovative scheduling strategy that utilizes the Proximal Policy Optimization (PPO) algorithm within DRL to achieve optimized resource allocation and enhanced utilization. It dynamically adjusts container deployment, reduces resource waste, and simultaneously enhances cluster performance and response speed.

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A Heterogeneous Multi-container Scheduling Mechanism Based on Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Junjie Li,
  • Wangdong Wu,
  • Ling Wang,
  • Lei Wang

摘要

Existing cloud computing container scheduling methods have significant deficiencies in resource utilization and performance. To address these issues and improve resource efficiency and cluster performance, we propose a heterogeneous multi-container scheduling mechanism based on Deep Reinforcement Learning (DRL). This technology employs an innovative scheduling strategy that utilizes the Proximal Policy Optimization (PPO) algorithm within DRL to achieve optimized resource allocation and enhanced utilization. It dynamically adjusts container deployment, reduces resource waste, and simultaneously enhances cluster performance and response speed.