移动Ad Hoc云中任务卸载的联合优化策略  

Joint Optimization Strategy for Task Offloading in Mobile Ad Hoc Cloud

作　　者：胡杨 韩嘉伟[3] 张志红 李军 刘畅 曾祥豹[1,2] 李小飞 王音心 HU Yang;HAN Jiawei;ZHANG Zhihong;LI Jun;LIU Chang;ZENG Xiangbao;LI Xiaofei;WANG Yinxin(Chongqing Acoustic-Optic-Electronic Co. Ltd, China Electronics Technology Group, Chongqing 401332,China;The 26th Institute of China Electronics Technology Group Corporation, Chongqing 400060, China;School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

机构地区：[1]中电科技集团重庆声光电有限公司,重庆401332 [2]中国电子科技集团公司第二十六研究所,重庆400060 [3]重庆邮电大学移动通信技术重庆市重点实验室,重庆400065

出　　处：《压电与声光》2019年第3期459-464,共6页Piezoelectrics & Acoustooptics

基　　金：重庆市科技重大主题专项集成电路产业基金资助项目(cstc2018jszx-cyztzxX0001);重庆市教委科学技术研究基金资助项目(KJQN201800642);重庆市科技创新领军人才基金资助项目(cstc2018kjcxljrc0084);重庆市重点产业共性关键技术创新专项基金资助(cstc2017zdcy-zdyf0607)

摘　　要：了降低移动AdHoc云中客户端卸载计算密集型任务过程中产生的计算能耗、传输能耗和任务时延,该文提出了一种联合优化算法。该算法首先基于计算能耗、通信能耗及任务时延进行建模;然后进行预估计,以选择更优的代理终端,并由此降低总的系统能耗与任务时延。仿真结果表明,相对于传统云算法,该算法在系统能耗和任务时延两方面均有显著提升。In order to reduce the computational energy consumption, transmission energy consumption and task delay generated in the process of computing-intensive tasks offloaded by the clients in mobile Ad Hoc cloud, a joint optimization algorithm is proposed in this paper. Firstly, the algorithm model is established based on the computational energy consumption, communication energy consumption and task delay. Then, the algorithm is pre-estimated to select a better proxy terminal, thereby reducing the total system energy consumption and task delay. The simulation results show that compared with the traditional cloud computing algorithm, the proposed algorithm has significant improvement in both system energy consumption and task delay.

关 键 词：移动AdHoc云 系统能耗 任务时延 卸载策略 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]