.png){kind=icon}
返回
Energy-Aware and Fair Multi-User Multi-Task Computation Offloading
.png){kind=icon}
PRE
AI
DOI：10.1109/ICNC57223.2023.10074189.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Enabling computation intensive tasks with low latency requirements on mobile devices considering their battery life time is one of the challenges in 5G networks. 5G is an era with massive device connectivities, therefore, a realistic offloading scenario should consider a dense and dynamic scenario with users' movements. Considering the powerful Central Processing Units (CPUs) of current mobile devices and Device-to-Device (D2D) communications, the available resources of adjacent devices can be exploited to fulfil the requirements of recent applications. However, in order to prevent over-exploiting behaviours of some users in a feasible cooperation offloading scenario, defining proper incentive mechanisms is a must. In this work, we propose a fair energy aware offloading framework considering the inter dependency relationships of computation tasks. Our scenario contains a dense small cell with a MEC attached to the base station (BS). In order to solve our mixed integer nonlinear offloading problem (MINLP), a genetic algorithm is employed. The simulation results show a significant reduction in used energy as well as completion of the tasks within the respective deadlines compared to the other benchmarks.
期刊
机构
引用论文
On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks缓存辅助D2D组播网络中计算卸载的设计.png)
IEEE ACCESS
IEEE ACCESSIF3.6
PREAI
分享收藏
Variable speed and constant frequency control of hydraulic wind turbine with energy storage system
OAAI
OAAI分享收藏D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D CollaborationD2D雾化: 通过网络辅助D2D协作实现节能和激励感知的任务卸载框架
PREAI
分享收藏分享收藏Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing面向移动边缘协同计算的移动感知联合任务调度与资源分配
PREAI
分享收藏
查看更多内容