Generation of realistic cloud access times for mobile application testing using transfer learning

Abstract 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.