Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems

Abstract-Modern IT systems frequently employ virtualization technology to maximize resource efficiency. By sharing physical resources, however, the virtualized storage used in such environments can quickly become a bottleneck. Performance modeling and evaluation techniques applied prior to system deployment help to avoid performance issues. In current practice, however, modeling I/O performance is usually avoided due to the increasing complexity of modern virtualized storage systems. In this paper, we present an automated modeling approach based on statistical regression techniques to analyze I/O performance and interference effects in the context of virtualized storage systems. We demonstrate our approach in three case studies creating performance models with two I/O benchmarks. The case studies are conducted in a real-world environment based on IBM System z and IBM DS8700 server hardware. Using our approach, we effectively create performance models with excellent prediction accuracy for both I/O-intensive applications and I/O performance interference effects with a mean prediction error up to 7%

Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems

Abstract-Modern IT systems frequently employ virtualization technology to maximize resource efficiency. By sharing physical resources, however, the virtualized storage used in such environments can quickly become a bottleneck. Performance modeling and evaluation techniques applied prior to system deployment help to avoid performance issues. In current practice, however, modeling I/O performance is usually avoided due to the increasing complexity of modern virtualized storage systems. In this paper, we present an automated modeling approach based on statistical regression techniques to analyze I/O performance and interference effects in the context of virtualized storage systems. We demonstrate our approach in three case studies creating performance models with two I/O benchmarks. The case studies are conducted in a real-world environment based on IBM System z and IBM DS8700 server hardware. Using our approach, we effectively create performance models with excellent prediction accuracy for both I/O-intensive applications and I/O performance interference effects with a mean prediction error up to 7%

A Study of I/O Performance of Virtual Machines

In this study, we investigate some counterintuitive but frequent performance issues that arise when doing high-speed networking (or I/O in general) with Virtual Machines (VMs). VMs use one or more single-producer/single-consumer systems to exchange I/O data (e.g. network packets) with their hypervisor. We show that when the producer and the consumer process packets at different rates, the high cost required for synchronization (interrupts and ‘kicks’) may reduce throughput of the system well below the slowest of the two parties; moreover, accelerating the faster party may cause the throughput to decrease. Our work provides a model for throughput, efficiency and latency of producer/consumer systems when notifications or sleeping are used as a synchronization mechanism; identifies different operating regimes depending on the operating parameters; validates the accuracy of our model against a VirtIO-based prototype, taking into account most of the details of real-world deployments; provides practical and robust strategies to maximize throughput and minimize energy while keeping the latency under control, without depending on precise timing measurements nor unreasonable assumptions on the system’s behavior. The study is particularly interesting for Network Function Virtualization deployments, where high-rate producer/consumer systems in virtualized environments are the core components

Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures

Model-based techniques are a powerful approach to engineering autonomic and self-adaptive systems. This thesis presents a model-based approach for proactive and autonomic performance-aware resource management in dynamic IT infrastructures. Core of the approach is an architecture-level modeling language to describe performance and resource management related aspects in such environments. With this approach, it is possible to autonomically find suitable system configurations at the model level

Modeling and Prediction of I/O Performance in Virtualized Environments

We present a novel performance modeling approach tailored to I/O performance prediction in virtualized environments. The main idea is to identify important performance-influencing factors and to develop storage-level I/O performance models. To increase the practical applicability of these models, we combine the low-level I/O performance models with high-level software architecture models. Our approach is validated in a variety of case studies in state-of-the-art, real-world environments