In the emerging era of Internet of Things (IoT), fog computing plays a critical role in serving delay-sensitive and location-aware applications. As a result, fog nodes are envisioned to be heavily deployed and form future distributed data centers. Powering fog nodes with green energy sources (such as solar and wind), not only helps in environmental and CO₂ emission control but also paves the way towards a “sustainable IoT technology”. However, the downside of green energy is its variation and unpredictability, which needs to be engineered. In this paper, we use the Lyapunov optimization technique to derive algorithms for dynamic dispatching of the users’ requests among the nearby fog nodes and remote data centers. The proposed algorithms take into account the time constraints of the requests and maintain the system stability while efficiently utilize the available green energy sources. Exhaustive simulation results, based on solar radiation data supplied by the Australian Bureau of Meteorology, confirm the efficiency of the proposed algorithms. In particular, in terms of service time, the number of deadline misses and green energy utilization, the proposed algorithms outperform the state-of-the-art alternative up to 6%, 17% and 12%, respectively.

在新兴的物联网（IoT）时代，雾计算在为时延敏感和位置感知的应用程序服务中扮演着至关重要的角色。结果，可以预见雾节点将被大量部署并形成未来的分布式数据中心。使用绿色能源（例如太阳能和风能）为雾节点供电，不仅有助于环境和CO ₂排放控制，但也为“可持续的物联网技术”铺平了道路。但是，绿色能源的缺点是它的变异性和不可预测性，需要进行设计。在本文中，我们使用Lyapunov优化技术来导出用于在附近的雾节点和远程数据中心之间动态分配用户请求的算法。所提出的算法考虑了请求的时间限制，并在有效利用可用绿色能源的同时保持系统稳定性。基于澳大利亚气象局提供的太阳辐射数据的详尽的仿真结果证实了所提出算法的效率。特别是在服务时间，错过最后期限和绿色能源利用方面，