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<p>With the evolution of cloud computing towards a multi-cloud architecture, cross-cloud resource scheduling faces challenges such as heterogeneous environment adaptation and slow dynamic load response. How to improve resource utilization while ensuring service quality has become a core challenge in the field of cloud management. To address this need, we propose the TSL-HRL intelligent scheduling framework, which integrates time-series feature modeling and hierarchical reinforcement learning. The framework utilizes a time-series mixing module to deeply mine the periodic fluctuations and burst demand features of computing, storage, and network resources. It integrates a dynamic state estimation module with Kalman filtering to capture real-time changes in resource supply and demand. Additionally, it constructs a high-level planning - low-level response hierarchical reinforcement learning architecture: the high-level Q-learning algorithm formulates a global long-term resource allocation strategy to ensure optimal overall scheduling, while the low-level A2C algorithm adjusts the execution plan based on real-time network fluctuations and node load, enabling fast adaptation to dynamic changes, forming a macro-micro collaborative decision mechanism. In experiments on the Multi-Cloud Service Composition Dataset and Google 2019 Cluster dynamic node scenarios, TSL-HRL effectively balanced resource utilization efficiency and scheduling real-time performance with its three-level architecture design of time-series feature extraction - dynamic state perception - hierarchical strategy optimization. The study shows that TSL-HRL provides a systematic solution for resource management in multi-cloud environments. Future research will focus on lightweight extensions for edge-cloud collaborative scenarios, multi-objective energy consumption optimization frameworks, and meta-learning-driven rapid adaptation technologies, promoting the application and generalization of intelligent resource scheduling technologies in real-world complex scenarios.</p>

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Computing power network dynamic resource scheduling integrating time series mixing dynamic state estimation and hierarchical reinforcement learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月4日

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  • Hong Liu,
  • Shulei Zhang,
  • Li Li,
  • Tingting Sun,
  • Wei Xue,
  • Xiaoxia Yao,
  • Yong Xu

摘要

With the evolution of cloud computing towards a multi-cloud architecture, cross-cloud resource scheduling faces challenges such as heterogeneous environment adaptation and slow dynamic load response. How to improve resource utilization while ensuring service quality has become a core challenge in the field of cloud management. To address this need, we propose the TSL-HRL intelligent scheduling framework, which integrates time-series feature modeling and hierarchical reinforcement learning. The framework utilizes a time-series mixing module to deeply mine the periodic fluctuations and burst demand features of computing, storage, and network resources. It integrates a dynamic state estimation module with Kalman filtering to capture real-time changes in resource supply and demand. Additionally, it constructs a high-level planning - low-level response hierarchical reinforcement learning architecture: the high-level Q-learning algorithm formulates a global long-term resource allocation strategy to ensure optimal overall scheduling, while the low-level A2C algorithm adjusts the execution plan based on real-time network fluctuations and node load, enabling fast adaptation to dynamic changes, forming a macro-micro collaborative decision mechanism. In experiments on the Multi-Cloud Service Composition Dataset and Google 2019 Cluster dynamic node scenarios, TSL-HRL effectively balanced resource utilization efficiency and scheduling real-time performance with its three-level architecture design of time-series feature extraction - dynamic state perception - hierarchical strategy optimization. The study shows that TSL-HRL provides a systematic solution for resource management in multi-cloud environments. Future research will focus on lightweight extensions for edge-cloud collaborative scenarios, multi-objective energy consumption optimization frameworks, and meta-learning-driven rapid adaptation technologies, promoting the application and generalization of intelligent resource scheduling technologies in real-world complex scenarios.