在物联网网上进行短视电子计算在线框架 (An Online Framework for Ephemeral Edge Computing in the Internet of Things)

In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the problem, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

翻译：在Tings Internet (IoT) 环境中,边缘计算可以在任何时间和任何地方启动。然而,在IoT 环境中,边缘计算会话往往时间短暂,也就是说,当当前应用程序使用完成或边缘设备因流动性等因素而离开系统时,这种会话会持续很短的时间,而且往往会因当前应用程序使用完成或边缘设备离开系统而中断。因此,在本文中,通过研究在IoT 中快速边缘计算的问题,可以考虑边端计算在有限时间内运行的情景。为此,提出了一个新的在线框架,其中源边缘节点从一个区域内的传感器上卸载其计算任务,从一个区域内的传感器到相邻的分布任务偏近的边缘节点,即当当前应用时间过短的计算系统结束或边缘设备离开系统时,这些时间往往会中断。因此,框架的在线边缘节点可以优化任务的分配,决定任务将哪个邻域用于任务,即使任务以在线方式披露到源端节点,而未来的任务到达的信息也未知。拟议的框架基本上是通过联合考虑最接近的通信和最接近的端点节点的节点的节点的节点分配速度来,从而实现初步的在线数据的计算,因此可以解决一个双向的双端任务问题。因此,因此,因此,拟议的双端任务可以解决一个拟议的双向的双向式的双向在线任务,因此,因此,因此,拟议的双向可提供双向式的平调的平调的平调的平调的平调的平调的平调问题是所有。