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 endusers 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.%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.
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