协同云网络中最优延迟-能量动态卸载方案

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-03-27 DOI:10.1145/3555776.3577767
Jui Mhatre, Ahyoung Lee
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引用次数: 0

摘要

像虚拟化和人工智能这样的新兴技术在移动设备上变得越来越流行。但缺乏处理这些申请所需的资源仍然是主要障碍。协作边缘和云计算是这个问题的解决方案之一。我们提出了一种多周期深度确定性策略梯度(MP-DDPG)算法,通过划分任务并将其卸载到协作云和边缘网络来寻找最优卸载策略,以降低能耗。结果表明,MP-DDPG在协同云网络中实现了最小的延迟和能耗。
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MP-DDPG: Optimal Latency-Energy Dynamic Offloading Scheme in Collaborative Cloud Networks
Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still major hurdle. Collaborative edge and cloud computing are one of the solutions to this problem. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy by partitioning the task and offloading it to the collaborative cloud and edge network to reduce energy consumption. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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