Fog computing provides a distributed computing paradigm that executes interactive and distributed applications, such as the Internet of Things (IoT) applications. Large-scale scientific applications, often in the form of workflow ensembles, have a distributed and interactive nature that demands a dispersed execution environment like fog computing. However, handling a large-scale application in heterogeneous environment of fog computing requires harmonizing heterologous resources over the continuum from the IoT to the cloud. This paper investigates offloading and task allocation problems for orchestrating the resources in a fog computing environment where the IoT application is considered in the form of workflow ensembles. We called Offload-Location a mechanism which has been designed to find offloading coalition structure alongside a matching algorithm for allocating the offloaded tasks to fog/cloud resources. The introduced solution attempts to minimize the execution time and minimize the price paid to servers for executing the tasks provided that Quality of Service (QoS) requirements of the ensemble’s deadline and budget are retaining. These objectives lead to maximizing the number of completed workflows of the ensemble as an ultimate goal. The appropriate performance of this mechanism is studied under different workflow applications and circumstances.

雾计算提供了一种分布式计算范式，可以执行交互式和分布式应用程序，例如物联网 (IoT) 应用程序。大规模科学应用程序，通常以工作流集成的形式，具有分布式和交互性，需要像雾计算这样的分散执行环境。然而，在雾计算的异构环境中处理大规模应用程序需要协调从物联网到云的连续统一体上的异构资源。本文研究了在雾计算环境中协调资源的卸载和任务分配问题，其中以工作流集成的形式考虑物联网应用。我们称之为Offload-Location一种旨在找到卸载联盟结构以及用于将卸载的任务分配给雾/云资源的匹配算法的机制。引入的解决方案试图最小化执行时间并最小化为执行任务而支付给服务器的价格，前提是集合的截止日期和预算的服务质量 (QoS) 要求保持不变。这些目标导致将集成的已完成工作流的数量最大化作为最终目标。在不同的工作流应用程序和环境下研究了该机制的适当性能。