Computing power network dynamic resource scheduling integrating time series mixing dynamic state estimation and hierarchical reinforcement learning
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View abstract on PubMed
Summary
This summary is machine-generated.The TSL-HRL framework enhances multi-cloud resource scheduling by integrating time-series analysis and hierarchical reinforcement learning. This intelligent system improves resource utilization and real-time performance in complex cloud environments.
Area Of Science
- Cloud Computing
- Artificial Intelligence
- Resource Management
Background
- Multi-cloud architectures present challenges in cross-cloud resource scheduling, including adapting to heterogeneous environments and slow dynamic load responses.
- Optimizing resource utilization while maintaining service quality is a critical issue in cloud management.
Purpose Of The Study
- To propose an intelligent scheduling framework, TSL-HRL, that addresses the challenges of multi-cloud resource scheduling.
- To improve resource utilization efficiency and scheduling real-time performance in dynamic multi-cloud environments.
Main Methods
- The TSL-HRL framework integrates time-series feature modeling and hierarchical reinforcement learning.
- A time-series mixing module extracts features from resource fluctuations and demands.
- A dynamic state estimation module with Kalman filtering captures real-time resource supply and demand.
- A hierarchical reinforcement learning architecture with Q-learning (high-level) and A2C (low-level) algorithms enables collaborative decision-making.
Main Results
- TSL-HRL effectively balanced resource utilization efficiency and scheduling real-time performance in experiments.
- The framework demonstrated robust performance on the Multi-Cloud Service Composition Dataset and Google 2019 Cluster dynamic node scenarios.
- The three-level architecture design proved effective for time-series feature extraction, dynamic state perception, and hierarchical strategy optimization.
Conclusions
- TSL-HRL offers a systematic solution for resource management in multi-cloud environments.
- Future work includes extensions for edge-cloud collaboration, energy optimization, and meta-learning for rapid adaptation.
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