Multi-dimensional Quality of Experience for Customized User Requirements

Designing an everyone-centric customized service system is a crucial stage in the intelligent transformation of the digital world. As such, Quality of Experience (QoE) for user requirements design has become an essential research topic. Among the existing works, part of them assumes user requirements through ideal distributions. Another portion uses real-world data, but they analyze it from the system side. Both do not give a pervasive description of user requirements to support future research efforts. To tackle the above challenges, based on the Service Requirements Zone (SRZ), we propose an extended integrated multi-dimensional QoE, Acceptable Performance Zone (APZ), which includes both preferred and acceptable user requirements. We also detail the eight Key Performance Indicators (KPIs) in the SRZ. To provide more math support to the user requirements, we adopt real-world cluster trace datasets from Alibaba for analysis, aiming to explore the characteristics of real-world user requirements. The results show that the characteristics of user tasks generally obey some specific distributions. Specifically, the task size and memory requirement both follow bimodal log-Gaussian distributions, whereas the delay and computing requirements follow unimodal log-Gaussian distributions. At the same time, the energy consumption follows the log-beta distribution.