Generation of realistic cloud access times for mobile application testing using transfer learning
نویسندگان
چکیده
The network Quality of Service (QoS) metrics such as the access time, bandwidth, and packet loss play an important role in determining Experience (QoE) mobile applications. Various factors like Radio Resource Control (RRC) states, Mobile Network Operator (MNO) specific retransmission configurations, handovers triggered by user mobility, load, etc. can cause high variability these QoS on 4G/LTE, WiFi networks, which be detrimental to application QoE. Therefore, exposing realistic is critical for a tester attempting predict its A viable approach testing using synthetic traces. main challenge generation traces diversity environments lack wide scope real calibrate generators. In this paper, we describe measurement-driven methodology based transfer learning with Long Short Term Memory (LSTM) neural nets solve problem. requires relatively short sample targeted environment adapt presented basic model new environments, thus simplifying generation. We present feature LTE cloud time models adapted diverse target trace size just 6000 samples measured over few tens minutes. demonstrate that generated from are capable accurately reproducing QoE metric distributions including their outlier values.
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ژورنال
عنوان ژورنال: Computer Communications
سال: 2021
ISSN: ['1873-703X', '0140-3664']
DOI: https://doi.org/10.1016/j.comcom.2021.03.010