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 the mobile application to realistic network QoS metrics is critical
for a tester attempting to predict its QoE. A viable approach is testing using
synthetic traces. The main challenge in the generation of realistic synthetic
traces is the diversity of environments and the 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.

本论文提出一种基于转移学习的测量驱动方法，使用 LSTM 神经网络，通过相对较短的样本将传统模型适应于新环境，简化了合成轨迹生成，并生成了适用于多样化目标环境的 WiFi 和 LTE 云存取时间模型的特征，演示了由这些模型生成的合成轨迹可以准确地复制应用程序 QoE 指标分布，包括它们的异常值。