ISSN 1000-1239 CN 11-1777/TP

• 网络技术 •

### 移动边缘计算任务卸载和基站关联协同决策问题研究

1. 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)
• 出版日期: 2018-03-01
• 基金资助:
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)

### Joint Task Offloading and Base Station Association in Mobile Edge Computing

Yu Bowen1, Pu Lingjun1,2, Xie Yuting1, Xu Jingdong1, Zhang Jianzhong1

1. 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)
• Online: 2018-03-01

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).