移动边缘计算任务卸载和基站关联协同决策问题研究

于博文; 蒲凌君; 谢玉婷; 徐敬东; 张建忠

doi:10.7544/issn1000-1239.2018.20170714

移动边缘计算任务卸载和基站关联协同决策问题研究

¹(南开大学计算机与控制工程学院天津 300071)
²(广东省大数据分析与处理重点实验室(中山大学) 广州 510006) (bowenyu@mail.nankai.edu.cn)

基金项目: 国家自然科学基金项目(61702287，61702288)；天津市自然科学基金项目(16JCQNJC00700)；南开大学基础科研业务项目(070-63171112)

详细信息

中图分类号: TP393.1
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出版历程
- 发布日期: 2018-02-28

Joint Task Offloading and Base Station Association in Mobile Edge Computing

¹(College of Computer and Control Engineering, Nankai University, Tianjin 300071)
²(Guangdong Key Laboratory of Big Data Analysis and Processing (Sun Yat-Sen University), Guangzhou 510006)

摘要

摘要: 为了缩小IoT应用的服务质量要求与IoT设备有限的计算资源之间的差距，提高设备与基站能源利用率，设计了基于超密集网络的移动边缘计算框架COMED，提出了一个结合任务卸载、设备-基站关联以及基站睡眠调度的在线优化问题，旨在最小化设备和基站的整体能量消耗，同时满足IoT应用的服务质量要求.针对这一在线优化问题，提出了一个基于李雅普诺夫优化理论的任务调度算法JOSA，该算法只使用当前时间片的系统信息进行调度.仿真实验证明了COMED框架具有良好的性能：1)与设备本地处理相比，系统整体节能30%以上，与DualControl算法相比平均节能10%~50％；2)算法的执行时间与IoT设备数量呈近似线性的关系.
- 移动边缘计算 /
- 任务卸载 /
- 基站睡眠 /
- 设备-基站关联 /
- NP难问题
Abstract: In order to narrow the gap between the requirements of IoT applications and the restricted resources of IoT devices and achieve devices energy efficiency, in this paper we design COMED, a novel mobile edge computing framework in ultra-dense mobile network. In this context, we propose an online optimization problem by jointly taking task offloading, base station (BS) sleeping and device-BS association into account, which aims to minimize the total energy consumption of both devicesand BSs, and meanwhile satisfies applications’ QoS. To tackle this problem, we devise an online Lyapunov-based algorithm JOSA by exploiting the system information in the current time slot only. As the core component of this algorithm, we resort to the loose-duality framework and propose an optimal joint task offloading, BS sleeping and device-BS association policy for each time slot. Extensive simulation results corroborate that the COMED framework is of great performance: 1) more than 30% energy saving compared with local computing, and on average 10%-50% energy saving compared with the state-of-the-art algorithm DualControl (i.e., energy-efficiency); 2) the algorithm running time is approximately linear proportion to the number of devices (i.e., scalability).
- mobile edge computing /
- task offloading /
- base station (BS) sleeping /
- device-BS association /
- NP-hard problem

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