A Fine-Grained Performance-Based Decision Model for Virtualization Application Solution

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.