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.

中文翻译：

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具有能量收集功能的在线分布式卸载和计算资源管理，适用于支持异构 MEC 的物联网


近年来，随着移动互联网和物联网的快速发展和融合，计算密集型、时延敏感的物联网应用（APP）正以前所未有的速度激增。移动边缘计算（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并实现绿色持久运行来显着改善用户体验。传统的中心化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，以及如何更灵活地利用EH制定异构任务卸载策略仍然是挑战。在本文中，我们研究了一种支持 EH 的 MEC 卸载系统，并提出了一种基于博弈论和扰动 Lyapunov 优化理论的在线分布式优化算法。该算法在线工作，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并提高处理效率，通过平衡电池能量水平、延迟和收入来设计卸载预筛选标准。进行了广泛的模拟以验证所提出方法的有效性和合理性。