FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

Ramneek; Seung Jun Cha; Sangheon Pack; Seung Hyub Jeon; Yeon Jeong Jeong; Jin Mee Kim; Sungin Jung

doi:10.1109/ACCESS.2020.3007747

FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

Ramneek, Seung Jun Cha, Sangheon Pack, Seung Hyub Jeon, Yeon Jeong Jeong, Jin Mee Kim, Sungin Jung

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

The proliferation of smart devices and the advancement of data-intensive services has led to explosion of data, which uncovers massive opportunities as well as challenges related to real-time analysis of big data streams. The edge computing frameworks implemented over manycore systems can be considered as a promising solution to address these challenges. However, in spite of the availability of modern computing systems with a large number of processing cores and high memory capacity, the performance and scalability of manycore systems can be limited by the software and operating system (OS) level bottlenecks. In this work, we focus on these challenges, and discuss how accelerated communication, efficient caching, and high performance computation can be provisioned over manycore systems. The proposed Fast, ExteNsible, and ConsolidatEd (FENCE) framework leverages the availability of a large number of computing cores and overcomes the OS level bottlenecks to provide high performance and scalability for intelligent big data processing. We implemented a prototype of FENCE and the experiment results demonstrate that FENCE provides improved data reception throughput, read/write throughput, and application processing performance as compared to the baseline Linux system.

Original language	English
Article number	9134769
Pages (from-to)	125423-125437
Number of pages	15
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.3007747
Publication status	Published - 2020

Keywords

IoT
Manycore systems
big data
edge computing
stream analytics

ASJC Scopus subject areas

Computer Science(all)
Materials Science(all)
Engineering(all)

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10.1109/ACCESS.2020.3007747

Cite this

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title = "FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing",

abstract = "The proliferation of smart devices and the advancement of data-intensive services has led to explosion of data, which uncovers massive opportunities as well as challenges related to real-time analysis of big data streams. The edge computing frameworks implemented over manycore systems can be considered as a promising solution to address these challenges. However, in spite of the availability of modern computing systems with a large number of processing cores and high memory capacity, the performance and scalability of manycore systems can be limited by the software and operating system (OS) level bottlenecks. In this work, we focus on these challenges, and discuss how accelerated communication, efficient caching, and high performance computation can be provisioned over manycore systems. The proposed Fast, ExteNsible, and ConsolidatEd (FENCE) framework leverages the availability of a large number of computing cores and overcomes the OS level bottlenecks to provide high performance and scalability for intelligent big data processing. We implemented a prototype of FENCE and the experiment results demonstrate that FENCE provides improved data reception throughput, read/write throughput, and application processing performance as compared to the baseline Linux system.",

keywords = "IoT, Manycore systems, big data, edge computing, stream analytics",

author = "Ramneek and Cha, {Seung Jun} and Sangheon Pack and Jeon, {Seung Hyub} and Jeong, {Yeon Jeong} and Kim, {Jin Mee} and Sungin Jung",

note = "Funding Information: This research was supported in part by National Research Foundation (NRF) Grant (No. 2020R1A2C3006786), and in part by Institute of Information and communications Technology Promotion (IITP) Grant (No. 2014-3-00035 and IITP-2019-2017-0-01633). Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2020",

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language = "English",

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AU - Jeong, Yeon Jeong

AU - Kim, Jin Mee

AU - Jung, Sungin

N1 - Funding Information: This research was supported in part by National Research Foundation (NRF) Grant (No. 2020R1A2C3006786), and in part by Institute of Information and communications Technology Promotion (IITP) Grant (No. 2014-3-00035 and IITP-2019-2017-0-01633). Publisher Copyright: © 2013 IEEE.

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FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

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Keywords

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