Joint Task Offloading and Base Station Association in Mobile Edge Computing
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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.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).
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