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An Energy-Aware High Performance Task Allocation Strategy in Heterogeneous Fog Computing Environments

机译：异构雾计算环境中的能量感知高性能任务分配策略

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摘要

Combining the Internet-of-Things (IoT) technology with cloud computing is a significant alternative for powering the utilization of computing resources in a connected environment. A grand challenge in communications is raised by the emergence of big data, due to the large-sized data transmissions and frequent data exchanges. Applying fog computing is considered an option for resolving the communication challenge. However, a high extent of available heterogeneous computing attached to fog computing servers leads to a restriction of the resource management. This Article addresses the resource management issue by proposing a novel approach - named Energy-aware Fog Resource Optimization (EFRO) model- to optimizing the utilization of connected devices in fog computing. We develop a heuristic algorithm minimizing both energy cost and time consumption in a holistic way. A salient feature of EFRO lies in the integration of the standardization and smart shift operations fueled by a hill-climbing mechanism to produce near-optimal resource allocation solutions. Experimental results demonstrate that our EFRO is adroit at making near-optimal decisions in managing resources in fog computing environments. In particular, EFRO boosts the energy efficiency of the existing MESF and RR schemes by 54.83 and 71.28 percent, respectively. EFRO shortens DECM's allocation-generation time by up to a factor of 507.
机译：结合云计算的互联网（物联网）技术是用于为连接环境中的计算资源的利用提供供电的重要替代方案。由于大小的数据传输和频繁的数据交换，大数据的出现提出了隆重的巨大挑战。应用雾计算被视为解决通信挑战的选项。然而，附加到雾计算服务器的高度可用异构计算导致资源管理的限制。本文通过提出一种新的方法 - 命名的能量感知雾资源优化（EFRO）模型来解决资源管理问题 - 优化雾计算中连接设备的利用率。我们开发一种启发式算法，以全面的方式最小化能量成本和时间消耗。 EFRO的突出特征在于爬山机制推动的标准化和智能换档操作的集成，以产生近最佳的资源分配解决方案。实验结果表明，我们的EFRO在雾化计算环境中管理资源方面采用近乎最佳决策。特别是，EFRO分别将现有MESF和RR计划的能效分别提高54.83和71.28％。 EFRO将DECM的分配时间缩短到507倍。

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