.png){kind=icon}
返回
Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.3390/mi12091011.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users. First, we formulate the problem for the application-driven tasks in edge computing by jointly considering the delays and the energy consumption. Based on that, we propose a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning by adding a preliminary sorting mechanism to realize the joint optimization. Specifically, we analyze the characteristics of application-driven tasks and propose a heuristic algorithm by introducing a new factor to determine the processing order of parallelism sub-tasks. Finally, extensive experiments validate the effectiveness and reliability of the proposed algorithm. To be specific, compared with the baseline strategies, the total cost reduction by ATOS can be up to 64.5% on average.
Keyword:
task offloading
edge computing
application-driven task
deep reinforcement learning
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:
3
论文数:
1.4W
被引数:
2.9W
机构
引用论文
分享收藏分享收藏A Greedy Algorithm for Task Offloading in Mobile Edge Computing System.png)
CHINA COMMUNICATIONS
CHINA COMMUNICATIONSIF3.1
PREAI
分享收藏Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network软件定义超密集网络中移动边缘计算任务卸载
PREAI
分享收藏
查看更多内容