An analytic performance model of disk arrays
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Machine Learning
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Storage Device Performance Prediction with CART Models
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Queueing models of RAID systems with maxima of waiting times
Performance Evaluation
VM3: Measuring, modeling and managing VM shared resources
Computer Networks: The International Journal of Computer and Telecommunications Networking
Communications of the ACM
Analytical and Simulation Modelling of Zoned RAID Systems
The Computer Journal
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
Modeling virtualized applications using machine learning techniques
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
Experimental evaluation of the performance-influencing factors of virtualized storage systems
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Modern virtualized environments are key for reducing the operating costs of data centers. By enabling the sharing of physical resources, virtualization promises increased resource efficiency with decreased administration costs. With the increasing popularity of I/O-intensive applications, however, the virtualized storage used in such environments can quickly become a bottleneck and lead to performance and scalability issues. Performance modeling and evaluation techniques applied prior to system deployment help to avoid such issues. In current practice, however, virtualized storage and its performance-influencing factors are often neglected or treated as a black-box. In this paper, we present a measurement-based performance prediction approach for virtualized storage systems based on optimized statistical regression techniques. We first propose a general heuristic search algorithm to optimize the parameters of regression techniques. Then, we apply our optimization approach and create performance models using four regression techniques. Finally, we present an in-depth evaluation of our approach in a real-world representative environment based on IBM System z and IBM DS8700 server hardware. Using our optimized techniques, we effectively create performance models with less than 7% prediction error in the most typical scenario. Furthermore, our optimization approach reduces the prediction error by up to 74%.