Untangling mixed information to calibrate resource utilization in virtual machines
Proceedings of the 8th ACM international conference on Autonomic computing
Ginpex: deriving performance-relevant infrastructure properties through goal-oriented experiments
Proceedings of the joint ACM SIGSOFT conference -- QoSA and ACM SIGSOFT symposium -- ISARCS on Quality of software architectures -- QoSA and architecting critical systems -- ISARCS
Find your best match: predicting performance of consolidated workloads
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Web workload generation challenges - an empirical investigation
Software—Practice & Experience
Model-driven network emulation with virtual time machine
Proceedings of the Winter Simulation Conference
A goal-oriented simulation approach for obtaining good private cloud-based system architectures
Journal of Systems and Software
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Next Generation Data Centers are transforming labor-inten- sive, hard-coded systems into shared, virtualized, automated, and fully managed adaptive infrastructures. Virtualization technologies promise great opportunities for reducing energy and hardware costs through server consolidation. However, to safely transition an application running natively on real hardware to a virtualized environment, one needs to estimate the additional resource requirements incurred by virtualization overheads.In this work, we design a general approach for estimating the resource requirements of applications when they are transferred to a virtual environment. Our approach has two key components: a set of microbenchmarks to profile the different types of virtualization overhead on a given platform, and a regression-based model that maps the native system usage profile into a virtualized one. This derived model can be used for estimating resource requirements of any application to be virtualized on a given platform. Our approach aims to eliminate error-prone manual processes and presents a fully automated solution. We illustrate the effectiveness of our methodology using Xen virtual machine monitor. Our evaluation shows that our automated model generation procedure effectively characterizes the different virtualization overheads of two diverse hardware platforms and that the models have median prediction error of less than 5% for both the RUBiS and TPC-W benchmarks.