云边协同中的资源调度优化

王淑玲; 孙杰; 王鹏; 杨爱东

doi:10.11959/j.issn.1000-0801.2023027

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云边协同中的资源调度优化

工程与应用 | 更新时间：2024-06-05

- 云边协同中的资源调度优化
- Resource scheduling optimization in cloud-edge collaboration
- 电信科学 2023年39卷第2期页码：163-170
- 作者机构：
- 作者简介：
  
  [ "王淑玲（1988- ），女，博士，亚信科技（中国）有限公司研发中心规划部规划总监，主要研究方向为网络通信、云网融合" ]
  [ "孙杰（1983- ），男，亚信科技（中国）有限公司研发中心云网规划部经理，主要研究方向为通信与5G网络智能化" ]
  [ "王鹏（1976- ），男，亚信科技（中国）有限公司研发中心高级总监，主要研究方向为通信业务支撑、大数据和人工智能" ]
  [ "杨爱东（1984- ），男，博士，亚信科技（中国）有限公司通信人工智能实验室首席数据科学家，主要研究方向为 5G 无线通信、大数据挖掘、机器学习及其应用" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023027
  中图分类号： TP393
- 网络出版日期：2023-02，
  
  纸质出版日期：2023-02-20
- 稿件说明：
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王淑玲, 孙杰, 王鹏, 等. 云边协同中的资源调度优化[J]. 电信科学, 2023,39(2):163-170.

Shuling WANG, Jie SUN, Peng WANG, et al. Resource scheduling optimization in cloud-edge collaboration[J]. Telecommunications science, 2023, 39(2): 163-170.
王淑玲, 孙杰, 王鹏, 等. 云边协同中的资源调度优化[J]. 电信科学, 2023,39(2):163-170. DOI： 10.11959/j.issn.1000-0801.2023027.

Shuling WANG, Jie SUN, Peng WANG, et al. Resource scheduling optimization in cloud-edge collaboration[J]. Telecommunications science, 2023, 39(2): 163-170. DOI： 10.11959/j.issn.1000-0801.2023027.

摘要

随着业务类型的丰富和多样化，低时延、高带宽、数据私密性、高可靠性等成为业务普遍的要求。边缘计算、雾计算、分布式云、算力网络等方案相继被提出，并在产学研各界引发了深度的研究和探索。针对“多级的算力分布以及算力的协同将是未来算力结构的主流”这一观点，产业内外达成了共识，算力管理、分配、调度等与资源优化相关的问题也成为当下的研究热点和重点攻关方向。为此，面向未来的算力供给结构，首先描述了学术界、产业界资源调度优化问题的最新进展，总结了当前的主要方法论和工程实施架构；然后，针对两种典型的云边协同场景，从场景拆分、调度目标、求解方案依次进行分析，给出了适应场景特性的资源调度优化参考方案。

Abstract

With the enrichment and diversification of business types

low latency

high bandwidth

data privacy and high reliability have become common requirements.Edge computing

fog computing

distributed cloud

computing power network and other solutions have been proposed

and have triggered in-depth research and exploration in industry

academia and research.There is a consensus within and outside the industry on the view that “multi-level computing power distribution and collaboration of computing power will be the mainstream of computing power structure in the future”.The problems related to resource scheduling optimization

such as computing power management

allocation

scheduling

have also become the current research hotspot and key research direction.Therefore

for the future computing power supply structure

focuses on the latest progress of resource scheduling optimization in academia and industry

the current main methodology and engineering implementation architecture was summarized.And then

for the two typical cloud edge collaboration scenarios

the analysis was carried out from the perspective of scene splitting

scheduling objectives

and solutions in turn

and the resource scheduling optimization reference schemes that adapted to the characteristics of the scenarios were analyzed and discussed respectively.

关键词

Keywords

references

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