Towards Computing Resource Reservation Scheduling in Industrial Internet of Things
Fan Liang, Wei Yu, Xing Liu, David Griffith, Nada T. Golmie
The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time-sensitive, while others are not, and more importantly, some tasks are non-preemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process time-sensitive tasks is a critical issue. Although some scheduling algorithms can deal with the latency requirements of time-sensitive tasks, they lack consideration for non-preemptive tasks. To address this issue, in this paper we propose a task scheduling scheme that reserves computing resources to wait for upcoming time-sensitive tasks in the IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for time-sensitive tasks. To evaluate the proposed algorithm, we have implemented a simulation platform and conducted extensive experiments. Our experimental results demonstrate the effectiveness of our approach, which can allocate computing resources so that the processing time for the time-sensitive tasks can be reduced.
, Yu, W.
, Liu, X.
, Griffith, D.
and , N.
Towards Computing Resource Reservation Scheduling in Industrial Internet of Things, IEEE Internet of Things, [online], https://dx.doi.org/10.1109/JIOT.2020.3044057, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930392
(Accessed September 22, 2021)