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边缘计算环境下应用驱动的网络延迟测量与优化技术

符永铨, 李东升

符永铨, 李东升. 边缘计算环境下应用驱动的网络延迟测量与优化技术[J]. 计算机研究与发展, 2018, 55(3): 512-523. DOI: 10.7544/issn1000-1239.2018.20170793
引用本文: 符永铨, 李东升. 边缘计算环境下应用驱动的网络延迟测量与优化技术[J]. 计算机研究与发展, 2018, 55(3): 512-523. DOI: 10.7544/issn1000-1239.2018.20170793
Fu Yongquan, Li Dongsheng. Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey[J]. Journal of Computer Research and Development, 2018, 55(3): 512-523. DOI: 10.7544/issn1000-1239.2018.20170793
Citation: Fu Yongquan, Li Dongsheng. Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey[J]. Journal of Computer Research and Development, 2018, 55(3): 512-523. DOI: 10.7544/issn1000-1239.2018.20170793
符永铨, 李东升. 边缘计算环境下应用驱动的网络延迟测量与优化技术[J]. 计算机研究与发展, 2018, 55(3): 512-523. CSTR: 32373.14.issn1000-1239.2018.20170793
引用本文: 符永铨, 李东升. 边缘计算环境下应用驱动的网络延迟测量与优化技术[J]. 计算机研究与发展, 2018, 55(3): 512-523. CSTR: 32373.14.issn1000-1239.2018.20170793
Fu Yongquan, Li Dongsheng. Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey[J]. Journal of Computer Research and Development, 2018, 55(3): 512-523. CSTR: 32373.14.issn1000-1239.2018.20170793
Citation: Fu Yongquan, Li Dongsheng. Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey[J]. Journal of Computer Research and Development, 2018, 55(3): 512-523. CSTR: 32373.14.issn1000-1239.2018.20170793

边缘计算环境下应用驱动的网络延迟测量与优化技术

基金项目: 国家“九七三”重点基础研究发展计划基金项目(2014CB340303);国家自然科学基金项目(61402509)
详细信息
  • 中图分类号: TP391

Application Driven Network Latency Measurement Analysis and Optimization Techniques Edge Computing Environment: A Survey

  • 摘要: 互联网、移动计算、物联网技术的进步推动了人-机-物环境的深度融合,催生了一大批面向边缘用户的网络搜索、在线社会网络、电子商务、视频监控、智能助理等类型的边缘计算应用.边缘计算应用具有规模巨大、服务质量敏感等特性,对延迟性能提出迫切需求,然而,由于用户访问请求跨边缘网络、广域网、数据中心异构环境,“长尾延迟”问题导致边缘用户的体验质量严重下降.首先综述边缘计算应用的系统架构特征,然后分析长尾延迟的产生原因,分类介绍网络延迟测量的主要理论和方法,并归纳对长尾延迟的优化技术,最后提出在线优化运行环境的思想以及面临的挑战.
    Abstract: The technical advancements of Internet, mobile computing and Internet of things (IoT) have been pushing the deep integration of human, machine and things, which fostered a lot of end-users oriented network search, online social networks, economical business, video surveillance and intelligent assistant tools, which are typically referred to as online data-intensive applications. These new applications are of large scale and sensitive to the service quality, requiring stringent latency performance. However, end-user requests traverse heterogeneous environments including edge network, wide-area network and the data center, which naturally incurs a long-tail latency issue that significantly degrades users’ experience quality. This paper surveys architectural characteristics of edge-computing applications, analyzes causes of the long-tail latency issue, categorizes key theories and methods of the network latency measurement, summarizes long-tail latency optimization techniques, and finally proposes the idea of constructing an online optimization runtime environment and discusses some open challenges.
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    其他类型引用(1)

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出版历程
  • 发布日期:  2018-02-28

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