Xen and the art of virtualization

Barham, Paul; Dragovic, Boris; Fraser, Keir; Hand, Steven; Harris, Tim; Ho, Alex; Neugebauer, Rolf

doi:10.1145/945461.945462

Cited by 1,981 publications

(1,123 citation statements)

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“…Xen [1,2] is a popular open-source x86 virtual machine monitor (VMM) based on virtualization technologies. Recent prevalent virtualization technologies, like full system virtualization adopted in VmWare [15] and para-virtualization are both supported by Xen which uses para-virtualization as a more efficient and lower overhead modes of virtualizations.…”

Section: A Xen I/o Overviewmentioning

confidence: 99%

Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments

Liu

Mei

et al. 2010

2010 IEEE 3rd International Conference on Cloud Computing

156

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Abstract-Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running networkintensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPUintensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPUintensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the indepth understanding of the interference overheads in I/O channel in the driver domain and VMM.

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Section: A Xen I/o Overviewmentioning

confidence: 99%

Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments

Liu

Mei

et al. 2010

2010 IEEE 3rd International Conference on Cloud Computing

156

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“…We describe the design and implementation of our algorithms, their success detecting a number of VMMs including VMware [23,25], the Xen VMM [2] on standard hardware, and the Xen VMM on a machine with hardware assistance for virtualization. We develop a class of VMM detectors that, when executing on a target system of unknown status (virtual or real) with access to a trusted external timer, can distinguish between a virtual and real target system (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Towards Sound Detection of Virtual Machines

et al.

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Summary. We design, implement, and evaluate a practical timing-based approach to detect virtual machine monitors (VMMs) without relying on VMM implementation details. The algorithms developed in this paper are based on fundamental properties of virtual machine monitors rather than easily modified software artifacts. We evaluate our approach against two common VMM implementations on machines with and without hardware support for virtualization in a number of remote and local experiments. We successfully distinguish between virtual and real machines in all cases even with incomplete information regarding the VMM implementation and hardware configuration of the targeted machine.

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“…One of the widely used cloud computing service models is Infrastructure as a Service (IaaS) [2] where raw computing resources are provided in the form of Virtual Machines (VMs) to cloud consumers charged for the resources consumed. Virtualization approaches such as Xen [3] and VMware [4] allow infrastructure resources to be shared in an effective manner. VMs also make it possible to allocate resources dynamically according to varying demands, providing opportunities for the efficient use of computing resources, as well as the optimization of application performance and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

2017

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Dynamic workloads in cloud computing can be managed through live migration of virtual machines from overloaded or underloaded hosts to other hosts to save energy and/or mitigate performance-related Service Level Agreement (SLA) violations. The challenging issue is how to detect when a host is overloaded to initiate live migration actions in time. In this paper, a new approach to make long-term predictions of resource demands of virtual machines for host overload detection is presented. To take into account the uncertainty of long-term predictions, a probability distribution model of the prediction error is built. Based on the probability distribution of the prediction error, a decision-theoretic approach is proposed to make live migration decision that take into account live migration overheads. Experimental results using the CloudSim simulator and PlanetLab workloads show that the proposed approach achieves better performance and higher stability compared to other approaches that do not take into account the uncertainty of long-term predictions and the live migration overhead.

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Xen and the art of virtualization

Cited by 1,981 publications

References 0 publications

Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments

Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments

Towards Sound Detection of Virtual Machines

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

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