• Title/Summary/Keyword: Open Source Edge Computing Platform

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Analysis of Open Source Edge Computing Platforms: Architecture, Features, and Comparison (오픈 소스 엣지 컴퓨팅 플랫폼 분석: 구조, 특징, 비교)

  • Lim, Huhnkuk;Lee, Heejin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.8
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    • pp.985-992
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    • 2020
  • Edge computing is a technology that can prepare for a new era of cloud computing. Edge computing is not a remote data center where data is processed and computed, but low-latency/high-speed computing is realized by adding computing power and data processing power to the edge side close to an access point such as a terminal device or a gateway. It is possible. The types of edge computing include mobile edge computing, fog computing, and cloudlet computing. In this article, we describes existing open source platforms for implementing edge computing nodes. By presenting and comparing the structure, features of open source edge platforms, it is possible to acquire knowledge required to select the best edge platform for industrial engineers who want to build an edge node using an actual open source edge computing platform.

A Study on the Image/Video Data Processing Methods for Edge Computing-Based Object Detection Service (에지 컴퓨팅 기반 객체탐지 서비스를 위한 이미지/동영상 데이터 처리 기법에 관한 연구)

  • Jang Shin Won;Yong-Geun Hong
    • KIPS Transactions on Computer and Communication Systems
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    • v.12 no.11
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    • pp.319-328
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    • 2023
  • Unlike cloud computing, edge computing technology analyzes and judges data close to devices and users, providing advantages such as real-time service, sensitive data protection, and reduced network traffic. EdgeX Foundry, a representative open source of edge computing platforms, is an open source-based edge middleware platform that provides services between various devices and IT systems in the real world. EdgeX Foundry provides a service for handling camera devices, along with a service for handling existing sensed data, which only supports simple streaming and camera device management and does not store or process image data obtained from the device inside EdgeX. This paper presents a technique that can store and process image data inside EdgeX by applying some of the services provided by EdgeX Foundry. Based on the proposed technique, a service pipeline for object detection services used core in the field of autonomous driving was created for experiments and performance evaluation, and then compared and analyzed with existing methods.

A Comparative Analysis of Domestic and Foreign Docker Container-Based Research Trends (국내·외 도커 컨테이너 기반 연구 동향 비교 분석)

  • Bae, Sun-Young
    • The Journal of the Korea Contents Association
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    • v.22 no.10
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    • pp.742-753
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    • 2022
  • Cloud computing, which is rapidly growing as one of the core technologies of the 4th industrial revolution, has become the center of global IT trend change, and Docker, a container-based open source platform, is the mainstream for virtualization technology for cloud computing. Therefore, in this paper, research trends based on Docker containers were compared and analyzed, focusing on studies published from March 2013 to July 2022. As a result of the study, first, the number of papers published by year, domestic and foreign research were steadily increasing. Second, the keywords of the study, in domestic research, Docker, Docker Containers, and Containers were in the order, and in foreign research, Cloud Computing, Containers, and Edge Computing were in the order. Third, in the frequency of publishing institutions to estimate research trends, the utilization was the highest in two papers of the Korean Next Generation Computer Society and the Korean Computer Accounting Society. In the overseas research, IEEE Communications Surveys & Tutorials, IEEE Access, and Computer were in the order. Fourth, in the research method, experiments 78(26.3%) and surveys 32(10.8%) were conducted in domestic research. In foreign research, experiments 128(43.1%) and surveys 59(19.9%) were conducted. In the experiment of implementation research, In domestic research, System 25(8.4%), Algorithm 24(8.1%), Performance Measurement and Improvement 16(5.4%) were in the order, In foreign research, Algorithm 37(12.5%), Performance Measurement and Improvement 17(9.1%), followed by Framework 26(8.8%). Through this, it is expected that it will be used as basic data that can lead the research direction of Docker container-based cloud computing such as research methods, research topics, research fields, and technology development.