In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

中文翻译：

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雾计算中的资源管理方法：综合综述

近年来，物联网（IoT）成为最流行的技术之一，它促进了物与人之间的新交互以提高生活质量。随着物联网的快速发展，雾计算范式正在成为一种有吸引力的解决方案，用于处理物联网应用程序的数据。在雾环境中，IoT应用由雾中的中间计算节点以及云数据中心中的物理服务器执行。另一方面，由于资源的局限性，资源的异质性，动态性以及雾环境的不可预测性，使得资源管理问题成为雾景观中需要考虑的具有挑战性的问题之一。尽管资源管理问题非常重要，但据我们所知，没有任何系统，在雾计算环境中对资源管理方法领域进行了全面而详细的调查。在本文中，我们以经典分类法的形式提供了有关雾环境中资源管理方法的系统文献综述（SLR），以认识到有关此重要主题的最新机制，并提供了公开的问题。提出的分类法分为六个主要领域：应用程序放置，资源调度，任务卸载，负载平衡，资源分配和资源供应。根据性能指标，案例研究，使用的技术和评估工具等重要因素，对资源管理方法进行了比较，并讨论了它们的优缺点。