The network Quality of Service (QoS) metrics such as the access time, the bandwidth, and the packet loss play an important role in determining the Quality of Experience (QoE) of mobile applications. Various factors like the Radio Resource Control (RRC) states, the Mobile Network Operator (MNO) specific retransmission configurations, handovers triggered by the user mobility, the network load etc. can cause high variability in these QoS metrics on 4G/LTE, and WiFi networks, which can be detrimental to the application QoE. Therefore, exposing mobile application to realistic network QoS metrics is critical for testers attempting to predict its QoE. A viable approach is testing using synthetic traces. The main challenge in generation of realisitc synthetic traces is the diversity of environments and lack of wide scope of real traces to calibrate the generators. In this paper, we describe a measurement-driven methodology based on transfer learning with Long Short Term Memory (LSTM) neural nets to solve this problem. The methodology requires a relatively short sample of the targeted environment to adapt the presented basic model to new environments, thus simplifying synthetic traces generation. We present this feature for realistic WiFi and LTE cloud access time models adapted for diverse target environments with a trace size of just 6000 samples measured over a few tens of minutes. We demonstrate that synthetic traces generated from these models are capable of accurately reproducing application QoE metric distributions including their outlier values.

网络服务质量（QoS）指标（例如访问时间，带宽和数据包丢失）在确定移动应用程序的体验质量（QoE）中起着重要作用。诸如无线电资源控制（RRC）状态，特定于移动网络运营商（MNO）的重传配置，由用户移动性触发的切换，网络负载等各种因素可能会导致4G / LTE和WiFi上这些QoS指标的高可变性网络，这可能对应用程序QoE有害。因此，将移动应用程序暴露于实际的网络QoS指标对于尝试预测其QoE的测试人员至关重要。一种可行的方法是使用合成迹线进行测试。产生真实的合成迹线的主要挑战是环境的多样性以及缺乏用于校准发生器的真实迹线的广泛范围。在本文中，我们描述了一种基于测量学习驱动的方法，该方法基于带有长期短期记忆（LSTM）神经网络的转移学习来解决此问题。该方法要求目标环境的样本相对较短，以使呈现的基本模型适应新环境，从而简化了合成迹线的生成。我们为现实的WiFi和LTE云访问时间模型提供了此功能，该模型适用于各种目标环境，在几十分钟内测得的痕迹大小仅为6000个样本。