The rapid expansion of omnipresent phenomena, exemplified by the Internet of Things (IoT), necessitates significant consideration of data volume and processing requirements. Cloud servers serve as the ultimate destination for IoT data. However, the centralized nature of cloud-based architecture may lead to communication limitations and reduced response times. By placing servers close to the sources and data producers, fog computing provides a viable solution to these problems. Consequently, fog computing reduces bandwidth consumption and cloud task density. Unlike cloud computing, fog computing relies on distributed computing and operates on limited computing power servers. This research focuses on augmenting user experience and interactive response in fog computing systems through optimal resource allocation and bandwidth management. Recent studies between 2020 and 2024 on resource allocation, application placement, and scheduling in fog computing environments are reviewed in this article. The analysis encompasses diverse focus points, evaluation parameters, system architectures, datasets, and simulation tools.

中文翻译：

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雾云环境中的资源分配：最先进的技术

以物联网 (IoT) 为代表的无所不在的现象迅速扩展，需要认真考虑数据量和处理要求。云服务器是物联网数据的最终目的地。然而，基于云的架构的集中式性质可能会导致通信限制和响应时间缩短。通过将服务器放置在靠近数据源和数据生产者的位置，雾计算为这些问题提供了可行的解决方案。因此，雾计算减少了带宽消耗和云任务密度。与云计算不同，雾计算依赖于分布式计算，并在计算能力有限的服务器上运行。这项研究的重点是通过优化资源分配和带宽管理来增强雾计算系统中的用户体验和交互响应。本文回顾了 2020 年至 2024 年关于雾计算环境中的资源分配、应用程序放置和调度的最新研究。分析涵盖不同的焦点、评估参数、系统架构、数据集和模拟工具。