TY - JOUR
T1 - In-Network Computing Powered Mobile Edge: Toward High Performance Industrial IoT
AU - Mai, Tianle
AU - Yao, Haipeng
AU - Guo, Song
AU - Liu, Yunjie
N1 - Funding Information:
Acknowledgment This research was supported by funding from the Hong Kong RGC Research Impact Fund (RIF) with Project No. R5060-19 and R5034-18; the General Research Fund (GRF) with Project No. 152221/19E; the Collaborative Research Fund (CRF) with Project No. C5026-18G; the National Natural Science Foundation of China (Grant 61872310); and the BUPT Excellent Ph.D. Student Foundation under Grant CX2020108.
Publisher Copyright:
© 1986-2012 IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Recently, the industrial Internet of Things (IoT) has quickly become a disruptive force reshaping how we live and work. Compared to the consumer Internet, the industrial IoT puts forward much higher performance requirements in terms of network and computing capacity. The industrial IoT system needs to process tons of data generated by millions of IoT sensors in real-time. Recently, with the advent of programmable network devices (e.g., SmartNIC, programmable switch), the in-network computing (INC) paradigm has received a large amount of attention. INC refers to offload application-specific tasks from end-host to network devices. Benefiting from the line-rate processing capacity of network devices, the INC paradigm presents superior performance in terms of high-throughput low-latency computing. Therefore, INC is considered a promising technique to meet performance requirements in the industrial IoT. However, while network devices are capable of performing application-specific tasks, they are still far from the universal computing platform. In this article, we propose an INC powered mobile edge architecture, where we offload lightweight critical tasks to the INC devices and leave the rest to the MEC. To identify critical tasks, we introduce the complex event processing (CEP) tool in our architecture. Also, we present two industrial IoT use cases to evaluate the feasibility of our architecture.
AB - Recently, the industrial Internet of Things (IoT) has quickly become a disruptive force reshaping how we live and work. Compared to the consumer Internet, the industrial IoT puts forward much higher performance requirements in terms of network and computing capacity. The industrial IoT system needs to process tons of data generated by millions of IoT sensors in real-time. Recently, with the advent of programmable network devices (e.g., SmartNIC, programmable switch), the in-network computing (INC) paradigm has received a large amount of attention. INC refers to offload application-specific tasks from end-host to network devices. Benefiting from the line-rate processing capacity of network devices, the INC paradigm presents superior performance in terms of high-throughput low-latency computing. Therefore, INC is considered a promising technique to meet performance requirements in the industrial IoT. However, while network devices are capable of performing application-specific tasks, they are still far from the universal computing platform. In this article, we propose an INC powered mobile edge architecture, where we offload lightweight critical tasks to the INC devices and leave the rest to the MEC. To identify critical tasks, we introduce the complex event processing (CEP) tool in our architecture. Also, we present two industrial IoT use cases to evaluate the feasibility of our architecture.
UR - http://www.scopus.com/inward/record.url?scp=85098755111&partnerID=8YFLogxK
U2 - 10.1109/MNET.021.2000318
DO - 10.1109/MNET.021.2000318
M3 - Journal article
AN - SCOPUS:85098755111
SN - 0890-8044
VL - 35
SP - 289
EP - 295
JO - IEEE Network
JF - IEEE Network
IS - 1
M1 - 9293092
ER -