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面向多工作流的基于容器的边缘微服务选择机制

邵苏杰 吴磊 钟成 郭少勇 卜宪德

邵苏杰, 吴磊, 钟成, 郭少勇, 卜宪德. 面向多工作流的基于容器的边缘微服务选择机制[J]. 电子与信息学报, 2022, 44(11): 3748-3756. doi: 10.11999/JEIT220267
引用本文: 邵苏杰, 吴磊, 钟成, 郭少勇, 卜宪德. 面向多工作流的基于容器的边缘微服务选择机制[J]. 电子与信息学报, 2022, 44(11): 3748-3756. doi: 10.11999/JEIT220267
SHAO Sujie, WU Lei, ZHONG Cheng, GUO Shaoyong, BU Xiande. Container Based Microservice Selection for Multi-workflow in Edge Computing Paradigm[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3748-3756. doi: 10.11999/JEIT220267
Citation: SHAO Sujie, WU Lei, ZHONG Cheng, GUO Shaoyong, BU Xiande. Container Based Microservice Selection for Multi-workflow in Edge Computing Paradigm[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3748-3756. doi: 10.11999/JEIT220267

面向多工作流的基于容器的边缘微服务选择机制

doi: 10.11999/JEIT220267
基金项目: 国家电网有限公司总部科技项目“城市电力地下管廊无线通信网络覆盖关键技术研究及应用”(5700-202113189A-0-0-00)
详细信息
    作者简介:

    邵苏杰:男,讲师,研究方向为边缘计算、物联网、智能电网和网络管理

    吴磊:男,硕士生,研究方向为资源调度、边缘计算

    钟成:男,教授级高级工程师,主要研究方向为光通信、电力光缆线路和数据网络等

    郭少勇:男,副教授,主要研究方向为区块链应用技术、边缘计算、能源互联网等

    卜宪德:男,高级工程师,主要研究方向为电力系统通信和物联网等

    通讯作者:

    邵苏杰 buptssj@bupt.edu.cn

  • 中图分类号: TN915; TP393

Container Based Microservice Selection for Multi-workflow in Edge Computing Paradigm

Funds: The Science and Technology Project of State Grid Corporation of China: Research and Application of Key Technologies for Wireless Communication Network Coverage of Urban Power Underground Pipe Gallery (5700-202113189A-0-0-00)
  • 摘要: 边缘计算已经成为物联网(IOT)的有效解决方案,微服务模型将物联网应用程序划分为一组松散耦合、相互依赖的细粒度微服务。由于边缘节点资源有限,并发请求争夺容器实例,如何在移动边缘计算环境下为复杂工作流应用的并发请求生成合适的微服务执行方案是一个需要解决的重要问题。为此,该文首先建立了基于容器的微服务选择架构,并构建了服务时延模型和网络资源消耗模型,以减少平均延迟和网络消耗。其次,提出一种基于优先级机制和改进蚁群的微服务选择算法(MS-PAC),利用任务截止时间优先分配紧急任务以保证延迟,并利用蚁群算法的信息素机制寻找全局最优解。实验表明,该算法能有效地降低平均时延和网络消耗。
  • 图  1  基于容器的微服务选择架构

    图  2  基准工作流

    图  3  按时完成请求数量比较(ψ∈[1.0, 5.0])

    图  4  总体网络消耗比较(ψ∈[1.0, 5.0])

    图  5  不同请求数量下平均时延比较

    图  6  不同请求数量下总体网络资源消耗

    图  7  不同请求数量下按时完成请求数比较

    表  1  仿真参数设置

    仿真参数
    竞争因子$ \lambda $4
    容器新建因子$ \gamma $5
    信息素权重$ \alpha $1.0
    启发式信息权重$ \beta $1.0
    全局局部蒸发参数$ {\rho _g} $0.1
    局部信息素蒸发参数$ {\rho _l} $0.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-14
  • 修回日期:  2022-08-06
  • 录用日期:  2022-08-09
  • 网络出版日期:  2022-08-11
  • 刊出日期:  2022-11-14

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