AP端WLAN分析
AP-side WLAN Analytics
摘要：
监控终端用户体验的网络性能对于无线网络的管理者来说至关重要，因为它可以使他们远程修改网络参数以改善终端用户体验。不幸的是，对于性能监控，管理员通常仅限于他们管理的接入点（AP）的日志。该信息不直接捕获可能阻碍站（STA）侧传输的因素。因此，测量这种度量的现有技术方法主要涉及主动测量。不幸的是，这种主动测量增加了业务负载，并且如果定期使用并且用于所有STA，则可能潜在地中断用户业务，从而使网络中的其他用户的性能恶化并且耗尽移动设备的电池。本论文实现了被动AP端网络分析。在论文的第一部分中，我提出了虚拟速度测试，一个基于测量的框架，使AP估计速度测试结果的任何相关的客户端仅基于AP侧的可观的。接下来，我提出了上行链路延迟显微镜（uScope），AP端的框架，用于估计WLAN上行链路延迟的任何相关STA和分解成其组成部分。与虚拟速度测试类似，uScope仅基于被动AP侧观察进行估计。我们在一个商用硬件平台上实现了这两个框架，并在大学校园和住宅区进行了广泛的现场试验。在超过100万次的测试中，这两个框架的估计精度低于10%。
英文摘要：
Monitoring the network performance experienced by the end-user is crucial for managers of wireless networks as it can enable them to remotely modify the network parameters to improve the end-user experience. Unfortunately, for performance monitoring, managers are typically limited to the logs of the Access Points (APs) that they manage. This information does not directly capture factors that can hinder station (STA) side transmissions. Consequently, state-of-the-art methods to measure such metrics primarily involve active measurements. Unfortunately, such active measurements increase traffic load and if used regularly and for all the STAs can potentially disrupt user traffic, thereby worsening performance for other users in the network and draining the battery of mobile devices. This thesis enables passive AP-side network analytics. In the first part of the thesis, I present virtual speed test, a measurement based framework that enables an AP to estimate speed test results for any of its associated clients solely based on AP-side observables. Next, I present Uplink Latency Microscope (uScope), an AP-side framework for estimation of WLAN uplink latency for any of the associated STAs and decomposition into its constituent components. Similar to virtual speed test, uScope makes estimations solely based on passive AP-side observations. We implement both frameworks on a commodity hardware platform and conduct extensive field trials on a university campus and in a residential apartment complex. In over 1 million tests, the two proposed frameworks demonstrate an estimation accuracy with errors under 10%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
