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EPRS: Experience-Prioritized Reinforcement Scheduler in Edge Clusters
Shuya Tan1, Tiancong Huang1, Enguo Zhu2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|February 27, 2026
Summary
A new container-based scheduling framework with an Experience-Prioritized Reinforcement Scheduler (EPRS) enhances load balancing in edge computing. This approach improves resource utilization and performance in dynamic edge environments.
Area of Science:
- Distributed Systems
- Artificial Intelligence
- Computer Science
Background:
- Edge computing environments are dynamic and heterogeneous, posing challenges for task scheduling.
- Conventional scheduling algorithms struggle with load fluctuations, leading to imbalance and underutilization.
- Effective load balancing is crucial for optimizing resource utilization in edge clusters.
Purpose of the Study:
- To propose a container-based edge cluster scheduling framework for enhanced load balancing.
- To introduce an Experience-Prioritized Reinforcement Scheduler (EPRS) for adaptive task scheduling.
- To improve multi-dimensional resource allocation in heterogeneous edge environments.
Main Methods:
- Developed a container-based scheduling framework with a real-time resource monitor.
- Implemented an Experience-Prioritized Reinforcement Scheduler (EPRS) using priority-driven sample selection.
- Optimized resource allocation by considering node metrics and task requirements in a Kubernetes-based cluster.
Main Results:
- The proposed framework significantly improved multi-dimensional load balancing performance.
- Achieved an average gain of 28.25% over existing reinforcement learning-based schedulers.
- Demonstrated a 29.78% improvement compared to traditional scheduling algorithms.
Conclusions:
- The EPRS-integrated framework effectively addresses load balancing challenges in edge computing.
- The approach enhances resource utilization and scheduling efficiency in dynamic edge environments.
- Validated through implementation and experiments on a Kubernetes-based edge cluster.
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