Human-centric Composite Quality Modeling and Assessment for Virtual Desktop Clouds

There are several motivations (e.g., mobility, cost, security) that are fostering a trend to transition users’ traditional desktops to thin-client based virtual desktop clouds (VDCs). Such a trend has led to a rising importance for human-centric performance modeling and assessment within user communities in industry and academia that are increasingly adopting desktop virtualization. In this paper, we present a novel reference architecture and its easily-deployable implementation for modeling and assessment of objective user QoE (Quality of Experience) within VDCs, without the need for expensive and time-consuming subjective testing. The architecture novelty is in our approach to integrate finite state machine representations for user workload generation, and slow-motion benchmarking with deep packet inspection of application task performance affected by network health i.e., QoS (Quality of Service) variations to derive a “composite quality” metric model of user QoE. We show how this metric is customizable to a particular user group profile with different application sets, and can be used to: (i) identify dominant performance indicators for troubleshooting bottlenecks, and (ii) effectively obtain both ‘absolute’ and ‘relative’ objective user QoE measurements needed for pertinent selection/adaptation of thin-client encoding configurations within VDCs. We validate the effectiveness of our composite quality modeling and assessment methodology using subjective and objective user QoE measurements in a real-world VDC featuring RDP/PCoIP thin-client protocols, and actual users for a virtual classroom lab use case within a federated university system.