Generation of Realistic Cloud Access Times for Mobile Application Testing using Transfer Learning
This addresses the challenge of diverse network conditions for mobile app testers, though it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of generating realistic synthetic network traces for mobile application testing by using a transfer learning approach with LSTM neural nets, requiring only 6000 samples to adapt models to new environments and accurately reproduce Quality of Experience metrics.
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.