.png){kind=icon}
返回
Dependency-Aware Dynamic Task Scheduling in Mobile-Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/MSN50589.2020.00134.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the popularity and development of the Internet of things (IoT), human life has been deeply affected. Because of the limitations of computation capability and battery capacity, it is difficult for IoT devices to support frequent and complex computing. Motivated by this challenge, many works attempt to upload tasks of IoT devices to the cloud center for computation. However, because of the limitation of distance and bandwidth, cloud computing is difficult to guarantee low latency. As a feasible solution, Mobile Edge Computing (MEC) has attracted more and more attention. Most existing works focus on the computation offloading strategy, while the task scheduling on edge servers is not studied in depth. The tasks uploaded by IoT devices are dynamic and random, and there are dependencies between these tasks. Therefore, it is difficult for edge servers to find a task scheduling scheme to minimize the task execution delay. In this paper, to solve the task scheduling problem of edge server in multi-server and multi-user MEC system, we propose a heuristic algorithm based on the following three scenarios: 1) Tasks uploaded by IoT devices is dynamic and uncertain. 2) There are dependencies between tasks. 3) The computation capability of the edge server is limited. Experimental results show that the proposed algorithm can significantly reduce the overall completion time of tasks and the average task execution delay in the edge server.
Keyword:
Task scheduling
Mobile Edge Computing system
dynamic task upload
computation capability
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
I
IF:
0
论文数:
6
被引数:
0
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method可信物联网系统Edge中的动态资源分配: 一种强化学习方法
PREAI
分享收藏Multi-User Offloading for Edge Computing Networks: A Dependency-Aware and Latency-Optimal Approach
PREAI
分享收藏
查看更多内容