Desktop workload study with implications for desktop cloud resource optimization

Desktop cloud is a new delivery model in which end users connect to virtual desktops running in remote data centers. This paradigm offers multiple benefits both in terms of manageability as well as efficiency improvements. However, realizing this potential requires better understanding of desktop workload and its implications for desktop consolidation. We analyze CPU and memory usage on a sample of 35 desktops using a fine-grained 10 second averaging interval. Results provide insights into achievable efficiency improvements from desktop consolidation as well as detailed autocorrelation and variability behavior as a function of number of aggregated desktops. We also propose an interactivity classification method leading to functional form suitable for estimating residual durations of interactivity states. This finding can be leveraged in on-line proactive management algorithms for desktop cloud optimization.