| 标题 |
 Multi-resource interleaving for task scheduling in cloud-edge system by deep reinforcement learning 通过深度强化学习实现云边缘系统任务调度的多资源交织
相关领域
计算机科学
强化学习
交错
云计算
调度（生产过程）
分布式计算
边缘设备
任务（项目管理）
GSM演进的增强数据速率
人工智能
操作系统
数学优化
系统工程
数学
工程类
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| 网址 | |
| DOI |
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| 其它 | Collaborative cloud–edge computing has been systematically developed to balance the efficiency and cost of computing tasks for many emerging technologies. To improve the overall performance of cloud–edge system, existing works have made progress in task scheduling by dynamically distributing the tasks with different latency thresholds to edge and cloud nodes. However, the relationship of multi-resource queueing among different tasks within a node is not well studied, which leaves the merit of optimizing the multi-resource queueing unexplored. To fill this gap and improve the efficiency of cloud–edge system, we propose DeepMIC, a deep reinforcement learning (DRL)-based multi-resource interleaving scheme for task scheduling in cloud–edge system. |
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