.png){kind=icon}
返回
Achieving Energy Efficiency Through Dynamic Computing Offloading in Mobile Edge-Clouds
.png){kind=icon}
PRE
AI
DOI：10.1109/MASS.2018.00038.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There is a fundamental and critical problem in modern mobile applications, in which the battery life of mobile devices is usually limited. Recently, some researchers prolong the life of batteries by offloading computation tasks to edgeservers which are deployed near the mobile devices. However, computing offloading causes extra delay, which may severely downgrade the user experience especially for the delay-sensitive applications. The dynamic nature of mobile devices and the limited computation capacity of edge-servers also bring another challenges for trade-off optimization between energy consumption and task completion latency. In this paper, we propose a dynamic computing offloading (DCL) problem, which aims to minimize the maximum energy consumption of the mobile devices with task computing latency constraints. in a Mobile Edge-Computing (MEC) network. To this end, we consider two complementary cases: offline case (we sacrifice response time to achieve better service results) and online case (where we have to make immediate offloading decision for each computation task arrived online). For the offline case, we propose an efficient RMCL algorithm, and prove that our RMCL method achieves at least O(log m/alpha + 1) of the optimum with high probability, where m is the number of computation tasks in a time slot, and a is a value depending on the minimum edge-server capacity and the maximum computation task demand, with alpha >= 1 under most practical situations. For the online case, we propose an algorithm, named OMCL, which considers a trade off between the latency and energy consumption. The performance of our proposed algorithms is evaluated by formal analysis and simulation on a small-scale system. The simulation results show that the algorithm can reduce the maximum energy consumption in a set of mobile devices by 40% compared with executing computation tasks locally.
Keyword:
Edge-computing
mobile edge computing
offloading
energy efficiency
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:
13
被引数:
0
机构
引用论文
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks5g异构网络中面向移动边缘计算的高能效卸载.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏Carbamoylimidazolium and thiocarbamoylimidazolium salts: novel reagents for the synthesis of ureas, thioureas, carbamates, thiocarbamates and amidesTetrahedron
TetrahedronIF0
PREAI
分享收藏分享收藏
查看更多内容