Fog/Edge computing emerges as a novel computing paradigm that harnesses resources in the proximity of the Internet of Things (IoT) devices so that, alongside with the cloud servers, provide services in a timely manner. However, due to the ever-increasing growth of IoT devices with resource-hungry applications, fog/edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications. Therefore, the application placement in the fog/edge computing environment, in which several distributed fog/edge servers and centralized cloud servers are available, is a challenging issue. In this article, we propose a weighted cost model to minimize the execution time and energy consumption of IoT applications, in a computing environment with multiple IoT devices, multiple fog/edge servers and cloud servers. Besides, a new application placement technique based on the Memetic Algorithm is proposed to make batch application placement decision for concurrent IoT applications. Due to the heterogeneity of IoT applications, we also propose a lightweight pre-scheduling algorithm to maximize the number of parallel tasks for the concurrent execution. The performance results demonstrate that our technique significantly improves the weighted cost of IoT applications up to 65% in comparison to its counterparts.

中文翻译：

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边缘和雾计算环境中并发物联网应用的应用布局技术

雾/边缘计算作为一种新颖的计算范式出现，它利用物联网 (IoT) 设备附近的资源，以便与云服务器一起及时提供服务。然而，由于物联网设备资源匮乏的应用不断增长，资源有限的雾/边缘服务器无法有效满足物联网应用的需求。因此，在有多个分布式雾/边缘服务器和集中式云服务器可用的雾/边缘计算环境中放置应用程序是一个具有挑战性的问题。在本文中，我们提出了一种加权成本模型，以在具有多个物联网设备、多个雾/边缘服务器和云服务器的计算环境中最小化物联网应用程序的执行时间和能耗。除了，提出了一种基于模因算法的新应用放置技术，为并发物联网应用做出批量应用放置决策。由于物联网应用的异构性，我们还提出了一种轻量级的预调度算法，以最大化并发执行的并行任务数量。性能结果表明，与同类技术相比，我们的技术将 IoT 应用程序的加权成本显着提高了 65%。