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Multicore-Aware Virtual Machine Placement in Cloud Data Centers
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AI
DOI：10.1109/TC.2016.2529629.png)
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Finding the best way to map virtual machines (VMs) to physical machines (PMs) in a cloud data center is an important optimization problem, with significant impact on costs, performance, and energy consumption. In most situations, the computational capacity of PMs and the computational load of VMs are a vital aspect to consider in the VM-to-PM mapping. Previous work modeled computational capacity and load as one-dimensional quantities. However, today's PMs have multiple processor cores, all of which can be shared by cores of multiple multicore VMs, leading to complex scheduling issues within a single PM, which the one-dimensional problem formulation cannot capture. In this paper, we argue that at least a simplified model of these scheduling issues should be taken into account during VM placement. We show how constraint programming techniques can be used to solve this problem, leading to significant improvement over non-multicore-aware VM placement. Several ways are presented to hybridize an exact constraint solver with common packing heuristics to derive an effective and scalable algorithm.
Keyword:
VM placement
VM consolidation
cloud computing
data center
optimization algorithms
constraint programming
期刊
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3.8
论文数:
5.4K
被引数:
9.8K
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