Intel Virtualization Technology
Computer
lmbench: portable tools for performance analysis
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
Quantifying the performance isolation properties of virtualization systems
Proceedings of the 2007 workshop on Experimental computer science
Characterization & analysis of a server consolidation benchmark
Proceedings of the fourth ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
On optimal decision for QoS-aware composite service selection
Expert Systems with Applications: An International Journal
vTestkit: A Performance Benchmarking Framework for Virtualization Environments
CHINAGRID '10 Proceedings of the The Fifth Annual ChinaGrid Conference
Virtualization performance: perspectives and challenges ahead
ACM SIGOPS Operating Systems Review
Analysis of the performance-influencing factors of virtualization platforms
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
Virt-LM: a benchmark for live migration of virtual machine
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
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Virtualization technology has been widely applied across a broad range of contemporary datacenters. While constructing a datacenter, architects have to choose a Virtualization Application Solution (VAS) to maximize performance as well as minimize cost. However, the performance of a VAS involves a great number of metric concerns, such as virtualization overhead, isolation, manageability, consolidation, and so on. Further, datacenter architects have their own preference of metrics correlate with datacenters' specific application scenarios. Nevertheless, previous research on virtualization performance either focus on a single performance concern or test several metrics respectively, rather than gives a holistic evaluation, which leads to the difficulties in VAS decision-making. In this paper, we propose a fine-grained performance-based decision model termed as VirtDM to aid architects to determine the best VAS for them via quantifying the overall performance of VAS according to datacenter architects' own preference. First, our model defines a measurable, in-depth, fine-grained, human friendly metric system with organized hierarchy to achieve accurate and precise quantitative results. Second, the model harnesses a number of classic Multiple Criteria Decision-Making (MCDM) methods, such as the Analytical Hierarchical Process (AHP), to relieve people's effort of deciding the weight of different metrics base on their own preference accordingly. Our case study addresses an decision process based on three real VAS candidates as an empirical example exploiting VirtDM and demonstrates the effectiveness of our VirtDM model.