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On-demand deployment for IoT applications
Journal of Systems Architecture ( IF 4.5 ) Pub Date : 2020-05-05 , DOI: 10.1016/j.sysarc.2020.101794
Jingbin Zhang , Meng Ma , Wei He , Ping Wang

The uncertainties of IoT-edges system and environment make challenges to applications in reliability, stability, and total latency, etc. With edge devices increasingly able to connect to cloud servers from anywhere, applications can potentially perform deployment changing at runtime to improve performance. In this paper, we propose a dynamic-programming based algorithm, named E-ODD, for the on-demand deployment of applications at runtime. When the device does not have enough computational resources, it offloads some movable tasks to the cloud to occupy additional resources. However, when it is difficult to occupy cloud resources or computational resources on the device become excessive, the device migrates tasks back. Besides, we present MODE, a generic and flexible middleware for edge cloud on-demand deployment. We propose a context model and detect corresponding complex events which trigger the deployment changes. Based on the event detection model, middleware can acquire changing information real-timely. We have successfully applied our middleware for ventricular fibrillation monitoring. Finally, experiments prove that our on-demand deployment model outperforms other selected models both in total latency and throughput, especially in dynamic environments.



中文翻译:

物联网应用的按需部署

物联网边缘系统和环境的不确定性给应用程序带来了可靠性,稳定性和总延迟等方面的挑战。随着边缘设备越来越能够从任何地方连接到云服务器,应用程序可能会在运行时执行部署更改以提高性能。在本文中,我们提出了一种基于动态编程的算法,称为E-ODD,用于在运行时按需部署应用程序。当设备没有足够的计算资源时,它将一些可移动任务卸载到云中以占用更多资源。但是,当难以占用云资源或设备上的计算资源变得过多时,设备会将任务迁移回去。此外,我们介绍了MODE,这是一种用于边缘云按需部署的通用且灵活的中间件。我们提出了一个上下文模型,并检测触发部署更改的相应复杂事件。基于事件检测模型,中间件可以实时获取变化的信息。我们已经成功地将我们的中间件应用于心室纤颤监测。最后,实验证明,我们的按需部署模型在总延迟和吞吐量方面都优于其他选定模型,

更新日期:2020-05-05
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