Resource Defragmentation Using Market-Driven Allocation in Virtual Desktop Clouds

• Published in: 2015 IEEE International Conference on Cloud Engineering pp. 246 - 255
• Main Authors: Calyam, Prasad, Seetharam, Sripriya, Homchaudhuri, Baisravan, Kumar, Manish
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2015
• Subjects: Bandwidth; Joints; Measurement; Optimization; Resource management; Scalability; Virtual machining
• DOI: 10.1109/IC2E.2015.37

Similar to memory or disk fragmentation in personal computers, emerging "virtual desktop cloud" (VDC) services experience the problem of data center resource fragmentation which occurs due to on-the-fly provisioning of virtual desktop (VD) resources. Irregular resource holes due to fragmentation lead to sub-optimal VD resource allocations, and cause: (a)decreased user quality of experience (QoE), and (b) increased operational costs for VDC service providers. In this paper, we address this problem by developing a novel, optimal "Market-Driven Provisioning and Placement" (MDPP) scheme that is based upon distributed optimization principles. The MDPP scheme channelizes inherent distributed nature of the resource allocation problem by capturing VD resource bids via a virtual market to explore soft spots in the problem space, and consequently defragments a VDC through cost-aware utility-maximal VD re-allocations or migrations. Through extensive simulations of VD request allocations to multiple data centers for diverse VD application and user QoE profiles, we demonstrate that our MDPP scheme outperforms existing schemes that are largely based on centralized optimization principles. Moreover, MDPP scheme can achieve high VDC performance and scalability, measurable in terms of a 'Net Utility' metric, even when VD resource location constraints are imposed to meet orthogonal security objectives.