Resource Leasing and the Art of Suspending Virtual Machines

Sotomayor, Borja; Montero, Rubén; Llorente, Ignacio; Foster, Ian

doi:10.1109/hpcc.2009.17

Cited by 101 publications

(50 citation statements)

References 18 publications

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“…There are some studies on the job scheduling algorithms and performance of virtual machines mapped to physical machines [11], [12], [13]. Particularly, the work by Stillwell [14] addressed a set of scheduling issues.…”

Section: Related Workmentioning

confidence: 99%

Efficiency Assessment of Parallel Workloads on Virtualized Resources

Delgado

Sadjadi

Fong

et al. 2011

2011 Fourth IEEE International Conference on Utility and Cloud Computing

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Abstract-In cloud computing, virtual containers on physical resources are provisioned to requesting users. Resource providers may pack as many containers as possible onto each of their physical machines, or may pack specific types and quantities of virtual containers based on user or system QoS objectives. Such elastic provisioning schemes for resource sharing may present major challenges to scientific parallel applications that require task synchronization during execution. Such elastic schemes may also inadvertently lower utilization of computing resources. In this paper, we describe the elasticity constraint effect and ripple effect that cause a negative impact to application response time and system utilization. We quantify the impact using real workload traces through simulation. Then, we demonstrate that some resource scheduling techniques can be effective in mitigating the impacts. We find that a tradeoff is needed among the elasticity of virtual containers, the complexity of scheduling algorithms, and the response time of applications.General Terms: scheduling, virtualization, parallel application I. INTRODUCTION The current trend in resource provisioning for utility and cloud computing is to provide compute resources as virtual resource containers using virtualization technologies. The most common realization is the use of virtual machine (VM) technology, in which a virtual container manager (VCM) abstracts and controls the physical resources allotted to one or more VMs. The VM abstraction simplifies the deployment of application environments across a wide range of physical systems. The VCM provides fine-grained control of the amount of shared resources to apportion to each VM when they are competing for them. This provides resource providers flexible resource allocation control to satisfy quality of service of requests and to build cost and profit models [1]. This flexibility also allows application execution to be tuned in different ways. For example, execution of a job proceeds faster than expected when its VM borrows idle CPU cycles from collocated VMs. Moreover, parallel applications need not limit the degree of parallelism to the number of physical machines.The simplicity of application deployment and the flexibility of virtual resource acquisition enabled by the Cloud is creating interest among users of CPU-intensive parallel applications [2]. However, this flexibility can present a unique challenge when scheduling and executing them.

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Section: Related Workmentioning

confidence: 99%

Efficiency Assessment of Parallel Workloads on Virtualized Resources

Delgado

Sadjadi

Fong

et al. 2011

2011 Fourth IEEE International Conference on Utility and Cloud Computing

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“…This means that by removing the selected VMs, A CPU becomes greater than or equal to zero. Second, the list of selected VMs is scanned backwards and the VMs which are still not causing the host overload are restored (lines [17][18][19][20][21][22][23][24][25][26][27][28]. The VMs suspended from each overloaded host are appended to the SV list (line 30).…”

Section: 32mentioning

confidence: 99%

“…Two extensions of this framework address issues such as energy consumption (the Green Cloud Scheduler [14]) or advance reservation (the Haizea scheduler [15]) in data centers. Haizea is a resource manager used either as a simulator or as a backend VM scheduler for OpenNebula [15,23,24,25,26]. Haizea defines different types of leases implemented as virtual machines [26], uses off-line VMs migration, and considers the VM management time overheads.…”

Section: Related Workmentioning

confidence: 99%

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A Filter-Based Dynamic Resource Management Framework for Virtualized Data Centers

Crăciun

Salomie

2017

Studia UBB Informatica

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Abstract. Data centers adapt their operation to changing run-time conditions using energy-aware and SLA-compliant resource management techniques. In this context, current paper presents a novel filter-based dynamic resource management framework for virtualized data centers. By choosing and combining properly software filters performing the scheduling and resource management operations, the framework may be used in what-if analysis. The framework is evaluated by simulation for deploying batch best-effort jobs with time-varying CPU requirements.

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“…OpenStack [7], Eucalyptus [8], Universidad Complutense de Madrid의 OpenNebula [9], 시카고 대학교의 Nimbus [10] 등이 있으며, Xen에서는 CLI (Command Line Interface) …”

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A Tool for Analyzing VM Creation Failure caused by Virtual Disk Faults

Ku¹,

Dai²

2012

Journal of the Korea Society of Computer and Information

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In this paper, we present a tool (named VMBootFailMonitor) to detect and analyze a failure of a VM boot creation caused by faults on virtual disks of a Xen-based VM. Also, we presents an architecture and detail analysis process of the virtual disk faults in our tool. Especially, VMBootFailMonitor provides a causual analysis result for a case of VM creation failure based on three modules which performs virtual disk analysis, virtualized system analysis and system log analysis. We also support a comparison result between boot times of normal VMs and fault•제1저자 : 구민오 •교신저자 : 민덕기

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Resource Leasing and the Art of Suspending Virtual Machines

Cited by 101 publications

References 18 publications

Efficiency Assessment of Parallel Workloads on Virtualized Resources

Efficiency Assessment of Parallel Workloads on Virtualized Resources

A Filter-Based Dynamic Resource Management Framework for Virtualized Data Centers

A Tool for Analyzing VM Creation Failure caused by Virtual Disk Faults

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