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User communities are rapidly transitioning their "traditional desktops" that have dedicated hardware and software installations into "virtual desktop clouds" (VDCs) that are accessible via thin-clients. To allocate and manage VDC resources for Internet-scale desktop delivery, existing work focuses mainly on managing server-side resources based on utility functions of CPU and memory loads, and do not consider network health and thin-client user experience. Resource allocations without combined utility-directed information of system loads, network health and thin-client user experience in VDC platforms inevitably results in costly guesswork and over-provisioning of resources.In this thesis, an analytical model i.e., "Utility-Directed Resource Allocation Model (U-RAM)" is presented to solve the combined utility-directed resource allocation problem within VDCs. The solution uses an iterative algorithm that leverages utility functions of system, network and human components obtained using a novel virtual desktop performance benchmarking toolkit i.e., "VDBench". The combined utility functions are used to direct decision schemes based on Kuhn-Tucker optimality conditions for creating user desktop pools and determining optimal resource allocation size/location. U-RAM is evaluated in a VDC testbed featuring: (a) popular user applications (Spreadsheet Calculator, Internet Browser, Media Player, Interactive Visualization), and (b) TCP/UDP based thin-client protocols (RDP, RGS, PCoIP) under a variety of user load and network health conditions. Evaluation results demonstrate that U-RAM solution maximizes VDC scalability i.e., 'VDs per core density', and 'user connections quantity', while delivering satisfactory thin-client user experience.