基于模型分割的云边协作任务调度方法

Chuanfu Zhang, Jing Chen, Wen Li, Hao Sun, Yudong Geng, Tianxiang Zhang, Mingchao Ji, Tonglin Fu
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引用次数: 0

摘要

随着云计算和人工智能的不断发展和结合应用,出现了一些新方法来减少云边协作环境中训练神经网络模型的任务执行时间。最有吸引力的方法是神经网络模型分割。然而,影响分割点的因素很多,如资源分配、系统能耗、负载平衡和网络带宽分配等。有些分割方法考虑的是最短的任务执行时间,这忽略了边缘资源的利用率,可能造成资源浪费。此外,这些因素很难测量,这给计算最佳分割点以实现最大资源利用率和最短任务执行时间的目标带来了挑战。为解决这一问题,本文提出了一种基于模型分割的云边协同任务调度方法(CECMS)。该方法首先分析影响模型分割点的因素,然后通过预执行方法获得影响分割点计算的精确因素。此外,还改进了多目标求解算法,计算出最优模型分割点。并根据最优模型分割点将任务分别卸载到边缘和云端。最后,通过实验验证了该方法的有效性。最后,通过仿真实验验证了 CECMS 方法的有效性。与动态自适应DNN手术(Dynamic Adaptive DNN Surgery,DADS)方法和自适应DNN推理加速框架算法与端-边-云协同计算算法(Andive DNN inference acceleration framework algorithm with end-edge-cloud collaborative computing algorithm,ADC)相比,CECMS通过综合考虑边缘资源的利用率,在优化任务执行时间方面取得了与DADS和ADC相同的效果,在最小化任务执行时间的同时也有效保证了资源的利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A cloud-edge collaborative task scheduling method based on model segmentation
With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.
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