数据与计算发展前沿 ›› 2022, Vol. 4 ›› Issue (4): 132-141.

CSTR: 32002.14.jfdc.CN10-1649/TP.2022.04.013

doi: 10.11871/jfdc.issn.2096-742X.2022.04.013

• 技术与应用 • 上一篇    下一篇

文献计量视角下我国云边协同及任务调度研究综述

卢居辉*(),吴文,林至贤   

  1. 厦门市美亚柏科乾坤院大数据架构研究中心,福建 厦门 361001
  • 收稿日期:2021-11-02 出版日期:2022-08-20 发布日期:2022-08-10
  • 通讯作者: 卢居辉
  • 作者简介:卢居辉,厦门美亚柏科乾坤院大数据架构研究中心,高级工程师,硕士,主要研究方向为大数据、智能优化调度理论与方法等。
    本文中主要负责论文的撰写、修改。
    LU Juhui, master student, is a senior engineer at Xiamen Meiya Pico Information Co.,Ltd. His main research interests include big data, intelligent optimization scheduling theory and methods.
    In this paper, he is mainly responsible for paper writing and editing.
    E-mail: brucelujuhui@163.com

A Review of Cloud-Edge Collaboration and Task Scheduling in China from the Perspective of Bibliometrics

LU Juhui*(),WU Wen,LIN Zhixian   

  1. Xiamen Meiya Pico Information Co.,Ltd, Xiamen, Fujian 361001, China
  • Received:2021-11-02 Online:2022-08-20 Published:2022-08-10
  • Contact: LU Juhui

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摘要：

【目的】新型算力网络体系的构建背景下,对基于云边协同的任务调度研究现状进行全面的分析,可以使研究者们能够快速明确当下的研究热点,通过聚焦研究这些热点,促进新型算力网络体系的构建。【方法】利用文献计量学和内容分析等方法,对选取期刊论文的外部特征和内容进行了详细的调查分析,总结了目前基于云边协同的任务调度研究领域的一些特点,并对接下来的研究做出相应的展望。【结果】目前对基于云边协同的任务调度研究是与时代前沿科技紧密贴合的。【结论】随着5G技术的迅猛发展,以及“万物互联”、“一体化大数据中心”概念的密集提及,接下来对于“云边协同”、“任务调度”等相关的算法研究、优化,物理资源、数据资源的调度、分配优化,构建并完善一体化大数据中心生态体系等势必进入新一轮的研究热潮。

关键词: 云边协同, 任务调度, 文献计量, 内容分析

Abstract:

[Objective] Under the background of constructing the new computing network system, a compre-hensive analysis of the research status of task scheduling based on cloud-edge collaboration enables researchers to quickly identify the current research hotspots. Researchers can promote the construction of the new generation computing network system by focusing on those hotspots. [Methods] According to bibliometrics and content analysis methods, a detailed investigation and analysis of the external characteristics and content of selected journal articles are presented. This paper also summarizes some characteristics of the current research field of task scheduling based on cloud-edge collaboration and makes corresponding prospects for the future research. [Results] Recent research on task scheduling based on cloud edge collaboration closely fits the cutting-edge technology of the times. [Conclusions] With the rapid development of 5G technology and the intensive mentioning of the concepts of “Internet of Everything” and “integrated big data center”, some research fields tend to attract extensive attention, such as the algorithms related to cloud-edge collaboration and task scheduling, scheduling and allocation optimization of physical resources and data resources, and construction and improvement of integrated big data center ecosystem.

Key words: cloud edge collaboration, task scheduling, biblio-metric method, content analysis