Mijuskovic Adriana, Chiumento Alessandro, Bemthuis Rob, Aldea Adina, Havinga Paul
Department of Pervasive Systems, University of Twente, 7522 NB Enschede, The Netherlands.
Department of Industrial Engineering and Business Information Systems, University of Twente, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2021 Mar 5;21(5):1832. doi: 10.3390/s21051832.
Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.
直接在云端处理物联网应用程序可能并非适用于每个物联网场景的最高效解决方案，尤其是对于对时间敏感的应用程序而言。一种很有前景的替代方案是使用雾计算和边缘计算，它们解决了管理终端设备所需的大数据带宽问题。这些范式要求在靠近数据源的地方处理大量生成的数据，而不是在云端。基于云的物联网环境的考虑因素之一是资源管理，它通常围绕资源分配、工作负载平衡、资源供应、任务调度和服务质量来实现性能提升。在本文中，我们回顾了可应用于云、雾和边缘计算的资源管理技术。本次综述的目的是为针对云/雾和边缘环境的资源管理算法提供一个指标评估框架。为此，我们首先探讨该领域资源管理技术的研究挑战。因此，我们对当前的研究贡献进行分类，以支持构建一个评估框架。主要贡献之一是对涉及资源管理技术的研究论文进行概述和分析。最后，本次综述强调了在云/雾/边缘范式中使用资源管理技术的机会。这种做法仍处于早期发展阶段，需要克服一些障碍。
