In-Network Computing Powered Mobile Edge: Toward High Performance Industrial IoT

Tianle Mai, Haipeng Yao, Song Guo, Yunjie Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9293092
Pages (from-to)289-295
Number of pages7
JournalIEEE Network
Volume35
Issue number1
DOIs
Publication statusPublished - 1 Mar 2021

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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