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Cloud computing has become a key infrastructure for global computing and digital transformation, but efficient and balanced resource scheduling remains a major challenge. Deep Reinforcement Learning (DRL) has shown promise in this field, yet struggles with high-dimensional data and sample efficiency. Spiking Neural Networks (SNN), inspired by biological neurons, offer low energy consumption and sparse computation, but practical applications in scheduling are still limited due to issues like feature loss and sample dependence. This paper proposes SpikingRM, a hybrid model that integrates SNN with DRL to leverage their respective strengths. The system encodes task and resource features into spike frequency and intensity, enabling efficient scheduling across heterogeneous resources such as CPU, memory, and GPU. Experiments based on the Google Cluster Data 2019 show that SpikingRM outperforms DeepRM by 10%–30% in scheduling speed, load balancing, and energy efficiency, and achieves 20%–35% gains over traditional algorithms like SJF and FCFS in key performance metrics.

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SpikingRM: Efficient Scheduling Algorithm Based on Spiking Neural Network and Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Xiubo Liang,
  • Shuwei Liu,
  • Hongzhi Wang,
  • Qifei Zhang,
  • Bin Zhao

摘要

Cloud computing has become a key infrastructure for global computing and digital transformation, but efficient and balanced resource scheduling remains a major challenge. Deep Reinforcement Learning (DRL) has shown promise in this field, yet struggles with high-dimensional data and sample efficiency. Spiking Neural Networks (SNN), inspired by biological neurons, offer low energy consumption and sparse computation, but practical applications in scheduling are still limited due to issues like feature loss and sample dependence. This paper proposes SpikingRM, a hybrid model that integrates SNN with DRL to leverage their respective strengths. The system encodes task and resource features into spike frequency and intensity, enabling efficient scheduling across heterogeneous resources such as CPU, memory, and GPU. Experiments based on the Google Cluster Data 2019 show that SpikingRM outperforms DeepRM by 10%–30% in scheduling speed, load balancing, and energy efficiency, and achieves 20%–35% gains over traditional algorithms like SJF and FCFS in key performance metrics.