QoE Inference and Improvement Without End-Host Control
QoE Inference and Improvement Without End-Host Control
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DOI：
10.1109/sec.2018.00011
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发表时间：
2018-10
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通讯作者：
Ashkan Nikravesh;Qi Alfred Chen;Scott Haseley;Xiao Zhu;Geoffrey Challen;Z. Morley Mao
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作者：
Ashkan Nikravesh;Qi Alfred Chen;Scott Haseley;Xiao Zhu;Geoffrey Challen;Z. Morley Mao
网络服务质量（QoS）并不总是转化为用户体验质量（QoE）。因此，在传统上对QoS信息进行操作的若干场景中，需要了解用户QoE。示例包括ISP的流量管理和操作系统的资源分配。但是今天这些系统缺乏测量用户QoE的方法。为了帮助解决这个问题，我们提出了离线生成的每应用模型映射应用程序独立的QoS指标应用程序特定的QoE指标。这使得任何能够观察应用的网络流量的实体（包括ISP和接入点）都能够推断应用的QoE。我们描述了如何为具有显著不同的QoE指标的许多不同应用生成这样的模型。我们为60个流行应用程序的常见用户交互生成模型。然后，我们证明了这些模型的实用性，通过实现一个QoE感知的流量管理框架，并评估它的WiFi接入点。我们的方法成功地提高了反映用户感知性能的QoE指标。首先，我们证明了对延迟敏感的应用程序的流量进行优先级排序可以分别提高46%和115%的响应速度和视频帧速率。其次，我们证明了一种新的带宽密集型应用程序的QoE感知带宽分配方案可以将多个用户的平均视频比特率提高高达23%。
Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.