Enforcing Performance Isolation Across Virtual Machines in Xen

Gupta, Diwaker; Cherkasova, Ludmila; Gardner, R.; Vahdat, Amin

doi:10.1007/11925071_18

Cited by 177 publications

(171 citation statements)

References 18 publications

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“…A migration requires the memory of a virtual machine to be copied from the source server to a target server. Typically, the "amount" of CPU overhead is directly proportional to the "amount" of I/O processing [15,16]. Supporting a migration causes CPU load on both the source and target servers.…”

Section: Impact Of Migration Overhead With a Workload Placement Contrmentioning

confidence: 99%

Resource pool management: Reactive versus proactive or let’s be friends

et al. 2009

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The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools. We employ a trace-based workload placement controller that uses historical information to periodically and proactively reassign workloads to servers subject to their quality of service objectives. A reactive migration controller is introduced that detects server overload and underload conditions. It initiates the migration of workloads when the demand for resources exceeds supply. Furthermore, it dynamically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage. A host-load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner. A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately. The study considers trade-offs between i) required capacity and power usage, ii) resource access quality of service for CPU and memory resources, and iii) the number of migrations. Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management. The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

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Section: Impact Of Migration Overhead With a Workload Placement Contrmentioning

confidence: 99%

Resource pool management: Reactive versus proactive or let’s be friends

et al. 2009

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“…NIC supports direct memory access (DMA) technique handles the target guest memory page directly. We will see that three address remappings and two memory allocation/deallocation operations are used for per packet receive and only two remappings are required for each packet transmit [10,7,11].…”

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

154

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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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“…To increase the control over I/O virtualization, various solutions were proposed; most of them used CPU scheduling to isolate the VMs from the performance perspective. In [11], the authors proposed to augment the Xen hypervisor with a set of mechanisms to account for and to control the CPU time spent on behalf of VMs doing I/O. In [17], the authors proposed an extension to the Xen credit-based scheduler improving its behavior in presence of multiple different applications with heavy I/O workloads, prioritizing the I/O bound ones.…”

Section: Related Workmentioning

confidence: 99%

Providing Performance Guarantees to Virtual Machines Using Real-Time Scheduling

Cucinotta

Dhaval

Faggioli

et al. 2011

Euro-Par 2010 Parallel Processing Workshops

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Abstract. In this paper we tackle the problem of providing Quality of Service guarantees to virtualized applications, focusing on computing and networking guarantees. We propose a mechanism for providing temporal isolation based on a CPU real time scheduling strategy. This allows not only to have control over the individual virtual machine throughput, but also on the activation latency and response-time by which virtualized software components react to external events. We show experimental results gathered on a real system validating the approach.

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Enforcing Performance Isolation Across Virtual Machines in Xen

Cited by 177 publications

References 18 publications

Resource pool management: Reactive versus proactive or let’s be friends

Resource pool management: Reactive versus proactive or let’s be friends

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

Providing Performance Guarantees to Virtual Machines Using Real-Time Scheduling

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