| 标题 |
 Task Merging and Scheduling for Parallel Deep Learning Applications in Mobile Edge Computing 移动边缘计算中并行深度学习应用的任务合并与调度
相关领域
计算机科学
深度学习
分布式计算
瓶颈
移动边缘计算
边缘计算
云计算
边缘设备
调度（生产过程）
移动设备
人工智能
GSM演进的增强数据速率
并行计算
嵌入式系统
操作系统
数学优化
数学
|
| 网址 | |
| DOI | |
| 其它 | Mobile edge computing enables the execution of compute-intensive applications, e.g. deep learning applications, on the end devices with limited computation resources. However, the deep learning applications bring the performance bottleneck in mobile edge computing, due to the movements of a large amount of data incurred by the large number of layers and millions of weights. In this paper, the computing model for parallel deep learning applications in mobile edge computing is proposed, by considering the occupancy allocation of processors, cost of context switch, and multi-processors in edge server and remote cloud. The problem of minimizing the completion time for deep learning applications is formulated, and the NP-hardness of the problem is proved. |
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