论文标题
通过选择性边缘执行来改善物联网分析
Improving IoT Analytics through Selective Edge Execution
论文作者
论文摘要
大量新兴的物联网应用程序依赖于机器学习程序来分析数据。在用户设备上执行此类任务可改善响应时间并节省网络资源。但是，由于功率和计算局限性，这些设备通常无法支持此类资源密集型例程，也无法准确执行分析。在这项工作中，我们建议通过利用边缘基础架构来提高分析的性能。我们设计了一种算法，该算法使IoT设备能够在本地执行其例程；然后将它们外包给Cloudlet服务器，只有当他们预测他们将获得显着的性能改善时。它使用近似双重亚级别方法，对系统参数的统计属性造成了最小的假设。我们的分析表明，我们提出的算法可以智能地利用Cloudlet，适应服务要求。
A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes network resources. However, due to power and computing limitations, the devices often cannot support such resource-intensive routines and fail to accurately execute the analytics. In this work, we propose to improve the performance of analytics by leveraging edge infrastructure. We devise an algorithm that enables the IoT devices to execute their routines locally; and then outsource them to cloudlet servers, only if they predict they will gain a significant performance improvement. It uses an approximate dual subgradient method, making minimal assumptions about the statistical properties of the system's parameters. Our analysis demonstrates that our proposed algorithm can intelligently leverage the cloudlet, adapting to the service requirements.
