Human-centric Composite Quality Modeling and Assessment for Virtual Desktop Clouds Yingxiao Xu, Prasad Calyam, David Welling, Saravanan Mohan, Alex Berryman, Rajiv Ramnath University of Missouri, Ohio Supercomputer Center/OARnet, The Ohio State University, USA; Fudan University, China
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[email protected] Abstract—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 ar- chitecture 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 domi- nant 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 Fig.