.png){kind=icon}
返回
Learning-Based Task Offloading for Delay-Sensitive Applications in Dynamic Fog Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2019.2943647.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fog computing has the potential to liberate the computation-intensive mobile devices by task offloading. In this paper, we propose an online learning based task offloading algorithm for delay-sensitive applications in dynamic fog networks, which combines with the Combinatorial Multi-Armed Bandits (CMAB) framework. First, the proposed algorithm learns the sharing computing resources of fog nodes at a negligible computational cost. Then, we aim to minimize the task's offloading latency by jointly optimizing the task allocation decision and the spectrum scheduling. Finally, simulation results show that the proposed algorithm achieves much better delay performance than the traditional Upper Confidence Bound (UCB) algorithm and maintains ultra-low offloading delay in dynamic system state.
Keyword:
Fog computing
multi-armed bandit
task offloading
delay minimization
combinatorial multi-armed bandits
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
.png){kind=icon}
Detour: Dynamic Task Offloading in Software-Defined Fog for IoT ApplicationsDetour: 物联网应用软件定义雾中的动态任务卸载
PREAI
分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Synthesis of phenothiazine-based porous organic polymer and its application to iodine adsorption
PREAI
分享收藏
查看更多内容