Scalable Correlation-aware Virtual Machine Consolidation Using Two-phase Clustering

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Title: Scalable Correlation-aware Virtual Machine Consolidation Using Two-phase Clustering
Authors: Li, Xi
Ventresque, Anthony
Iglesias, Jesus Omana
Murphy, John
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Date: 24-Jul-2015
Online since: 2015-10-02T10:07:12Z
Abstract: Server consolidation is the most common and effective method to save energy and increase resource utilization in data centers, and virtual machine (VM) placement is the usual way of achieving server consolidation. VM placement is however challenging given the scale of IT infrastructures nowadays and the risk of resource contention among co-located VMs after consolidation. Therefore, the correlation among VMs to be co-located need to be considered. However, existing solutions do not address the scalability issue that arises once the number of VMs increases to an order of magnitude that makes it unrealistic to calculate the correlation between each pair of VMs. In this paper, we propose a correlation-aware VM consolidation solution ScalCCon1, which uses a novel two-phase clustering scheme to address the aforementioned scalability problem. We propose and demonstrate the benefits of using the two-phase clustering scheme in comparison to solutions using one-phase clustering (up to 84% reduction of execution time when 17, 446 VMs are considered). Moreover, our solution manages to reduce the number of physical machines (PMs) required, as well as the number of performance violations, compared to existing correlation-based approaches.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Institute of Electrical and Electronic Engineers (IEEE)
Copyright (published version): 2015 IEEE
Keywords: ClusteringConsolidationCorrelationPerformance degradationScalabilityVM placement
DOI: 10.1109/HPCSim.2015.7237045
Language: en
Status of Item: Peer reviewed
Conference Details: 2015 International Conference on High Performance Computing & Simulation (HPCS), Amsterdam, the Netherlands, 20 - 24 July, 2015
Appears in Collections:Computer Science Research Collection
PEL Research Collection

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