Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end-user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache-based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes.

中文翻译：

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一种基于缓存的改进雾计算调度的方法

雾计算是一种很有前途的技术，它通过使存储和计算资源靠近最终用户设备并具有额外的好处，例如改进执行时间和处理，来减少物联网 (IoT) 生态系统的延迟和功耗问题。然而，随着物联网设备的增加，资源分配和作业调度由于有限和异构的资源以及这种计算环境中的局部性限制而成为一项复杂而繁琐的任务。因此，本文提出了一种基于缓存的方法，用于在雾计算环境中有效分配资源，同时保持服务质量。所提出的算法是使用 iFogSim 模拟器实现的，并与传统的先到先服务和最短作业优先策略进行了全面比较。