Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT
Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT
复制标题
支持异构MEC的物联网的在线分布式卸载和计算资源管理以及能量收集
DOI：
10.1109/twc.2021.3076201
复制
发表时间：
2021-10-01
影响因子：
10.4
通讯作者：
Mao, Shiwen
中科院分区：
文献类型：
--
作者：
Xia, Shichao;Yao, Zhixiu;Mao, Shiwen
随着移动的互联网和物联网（IoT）的快速发展和融合，计算密集型和延迟敏感型的物联网应用（APP）近年来正以前所未有的速度激增。移动的边缘计算（MEC）和能量收集（EH）技术可以通过将计算任务卸载到边缘云服务器以及实现绿色和持久运行来显著改善用户体验。传统的集中式策略需要精确的系统状态信息，这在大数据和人工智能时代可能不可行。为此，如何按需分配有限的边缘云计算资源，以及如何以更灵活的方式利用EH开发异构任务卸载策略是仍然存在的挑战。本文研究了一个具有EH功能的MEC卸载系统，提出了一种基于博弈论和扰动李雅普诺夫优化理论的在线分布式优化算法。该算法在线工作，并共同确定异构任务卸载，按需计算资源分配和电池能量管理。此外，为了减少不必要的通信开销，提高处理效率，卸载预筛选标准的设计，通过平衡电池的能量水平，延迟，和收入。仿真结果验证了该方法的有效性和合理性。
With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.