利用人工神经网络的雾云智能任务分配

M. Pourkiani, Masoud Abedi
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引用次数: 2

摘要

为了减少雾云组合场景下的响应时间和网络带宽占用,提出了雾云智能任务分配(FCSTD)方法,根据应用需求在雾云服务器和云服务器之间智能分配任务。该方法使用人工神经网络来预测响应时间和结果的大小,然后根据预测的数量来分配任务。为了研究FCSTD的性能,我们将其应用于实际案例研究(这是一个延迟敏感的在线医疗保健应用程序,用于监视人们的健康状态),并分析了不同类型任务分布的性能。研究结果表明,与文献中提出的其他方法相比,FCSTD在降低互联网带宽利用率和响应时间方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FCSTD: Fog-Cloud Smart Task Distribution by Exploiting the Artificial Neural Networks
In order to reduce the response time and the Internet bandwidth utilization in combined Fog-Cloud scenarios, we propose Fog-Cloud Smart Task Distribution (FCSTD) method, which intelligently distributes the tasks between the fog and cloud servers with regard to the application requirements. This approach uses Artificial Neural Networks for predicting the response time and the size of the results and then distributes the tasks by considering the predicted amounts. To investigate the performance of FCSTD, we applied it to a real-world case study (which is a delay-sensitive online healthcare application that monitors the health status of people) and analyzed its performance for the distribution of different types of tasks. The achieved results show that FCSTD provides better performance for reducing the Internet bandwidth utilization and response time in comparison to the other proposed methods in the literature.
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