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Publication Citation: Comparing VM-Placement Algorithms for On-Demand Clouds

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Author(s): Kevin L. Mills; James J. Filliben; Christopher E. Dabrowski;
Title: Comparing VM-Placement Algorithms for On-Demand Clouds
Published: November 29, 2011
Abstract: Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or tradeoffs between honoring service-level agreements and constraining provider operating costs. Even where similar problems are addressed, each individual research team evaluates proposed algorithms under distinct conditions, using various techniques, often targeted to a small collection of VMs and PMs. In this paper, we describe an objective method that can be used to compare VM-placement algorithms in large clouds, covering tens of thousands of PMs and hundreds of thousands of VMs. We demonstrate our method by comparing 18 algorithms for initial VM placement in on-demand infrastructure clouds. We compare algorithms inspired by open-source code for infrastructure clouds, and by the online bin-packing literature.
Conference: IEEE CloudCom 2011
Proceedings: 3rd International Conference on Cloud Computing Technology and Science
Pages: pp. 91 - 98
Location: Athens, -1
Dates: November 29-December 1, 2011
Keywords: cloud computing; resource allocation; simulation
Research Areas: Modeling
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