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How Edge Computing and Initial Congestion Window Affect Latency of Web-Based Services: Early Experiences with Baidu?

机译:边缘计算和初始拥塞窗口如何影响基于Web的服务的延迟:百度的早期经验?

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More and more things, generating huge data, will come into and enrich our lives, and the Web of Things (WoT) as a guide allows these things to be part of the World Wide Web (WWW), by using various data analysis services on the WWW. However, based on our observation on the image recognition and searching service of Baidu, pure image data transmission costs hundreds of milliseconds, besides the time of connection establishment. Inspired by the emerging Edge Computing, we analyzed the relationship between time consumption and different service provider's locations, as well as different initial congestion windows of the Transmission Control Protocol (TCP), which affect web-based services' performance. Based on our experiments in different scenarios (i.e., initial congestion window, speed of connection device and server location), we found that pushing services to the edge of network and increasing initial congestion window, both of them can reduce latency on connection establishment and data transmission, especially when users are traveling at a high speed.
机译:越来越多的事物产生大量数据,将进入并丰富我们的生活,而物联网(WoT)作为指南,通过在Web上使用各种数据分析服务,可以使这些事物成为万维网(WWW)的一部分。万维网。但是,根据我们对百度图像识别和搜索服务的观察,纯图像数据传输除了建立连接的时间外还花费了数百毫秒。受新兴边缘计算的启发,我们分析了时间消耗与不同服务提供商位置以及传输控制协议(TCP)的不同初始拥塞窗口之间的关系,这些影响了基于Web的服务的性能。根据我们在不同情况下的实验(例如,初始拥塞窗口,连接设备和服务器位置的速度),我们发现将服务推送到网络边缘并增加初始拥塞窗口,它们两者都可以减少建立连接和数据的延迟传输,特别是当用户高速旅行时。

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