Parallax

Meyer, Dutch T.; Aggarwal, Gitika; Cully, Brendan; Lefebvre, Geoffrey; Feeley, Michael J.; Hutchinson, Norman C.; Warfield, Andrew

doi:10.1145/1352592.1352598

Cited by 71 publications

(4 citation statements)

References 7 publications

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“…Parallax [7], [8] uses a custom mechanism for storing VM images and creating snapshots. Template images are used to build new VM images that share common blocks.…”

Section: Related Workmentioning

confidence: 99%

Multi-layered Virtual Machines for Security Updates in Grid Environments

Schwarzkopf

Schmidt

Fallenbeck

et al. 2009

2009 35th Euromicro Conference on Software Engineering and Advanced Applications

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Abstract-The use of user specific virtual machines (VMs) in Grid and Cloud computing reduces the administration overhead associated with manually installing required software for every user on every computational resource. However, a large number of user specific VMs increases the risk of security attacks. In particular, Cloud computing providers like Amazon suffer from these problems, since they offer different operating systems within VMs and delegate the security update problem for VMs to the users. In this paper, a solution that solves the problem by separating a VM into several layers is presented. The approach creates the possibility of installing security updates into a base layer centrally, affecting all VMs without affecting the users' own installed software stack by merging package databases. The proposal permits resource providers to keep a large number of VMs patched with the latest security fixes without bothering the users. Furthermore, the proposal avoids the overhead for transferring possible large VM images over the network between the nodes of a Grid or Cloud by allowing to hold locally cached VM images with a basic operating system installation while only the user-specific software stack stored in a separate layer needs to be transferred.

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“…Parallax [7], [8] uses a custom mechanism for storing VM images and creating snapshots. Template images are used to build new VM images that share common blocks.…”

Section: Related Workmentioning

confidence: 99%

Multi-layered Virtual Machines for Security Updates in Grid Environments

Schwarzkopf

Schmidt

Fallenbeck

et al. 2009

2009 35th Euromicro Conference on Software Engineering and Advanced Applications

View full text Add to dashboard Cite

show abstract

“…Furthermore, the degree of machine consolidation has grown considerably under the influence of high performance hardware and sophisticated software techniques such as para-virtualization. Along with the emergence of cloud computing [1] and virtual desktop infrastructure [2,3], an individual computing environment is encapsulated in a virtual machine (VM) that is stored and managed in a server farm. Such virtualized environments accommodate unpredictable workloads of diverse domains ranging from desktop computing to scientific computation.…”

Section: Introductionmentioning

confidence: 99%

“…Its computing capability is also outstanding especially for floating point calculation; 1,331 GFLOPS for single precision and 665 GFLOPS for double precision. Evolving from traditional APIs such as the OpenGL and the Direct3D of Microsoft to program GPU as a graphics device, the CUDA [1] of NIVDIA and the OpenCL [5] provide more general programming environment for users. By supporting memory access model, interfaces to access GPUs directly and programming toolkits, these GPU frameworks contribute greatly to lead an era of general purpose computation on graphics processing units (GPGPU) [6].…”

Section: Introductionmentioning

confidence: 99%

GPU Virtualization using PCI Direct Pass-Through

Lee

Choi

2013

AMM

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Machine virtualization and cloud computing environment have highlighted for last several years. This trend is based on the endeavor to enhance the utilization and reduce the ownership cost of machines. On the other hand, in aspect of high performance computing, graphics processing unit (GPU) has proved its capability for general purpose computing in many research areas. Evolving from traditional APIs such as the OpenGL and the Direct3D to program GPU as a graphics device, the CUDA of NIVDIA and the OpenCL provide more general programming environment for users. By supporting memory access model, interfaces to access GPUs directly and programming toolkits, users can perform parallel computation using the hundreds of GPU cores. In this paper, we propose a GPU virtualization mechanism to exploit GPU on virtualized cloud computing environment. Differently from the previous work which mostly reimplemented GPU programming APIs and virtual device drivers, our proposed mechanism uses the direct pass-through of PCI-E channel having GPU. The main limitation of previous approaches is virtualization overhead. Since they were focused on the sharing of GPU among virtual machines, they reimplemented GPU programming APIs at virtual machine monitor (VMM) level, and it incurred significant performance overhead. Moreover, if APIs are changed, they need to reengineer the most of APIs. In our approach, bypassing virtual machine monitor layer with negligible overhead, the mechanism can achieve similar computation performance to bare-metal system and is transparent to the GPU programming APIs.

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“…The prevalent technique to alleviate this problem is incremental checkpointing [1,23,26,27]. In incremental checkpointing, the first snapshot contains a complete image of the VM's memory.…”

Section: Introductionmentioning

confidence: 99%

Fast and space-efficient virtual machine checkpointing

Park

Egger

Lee

2011

Proceedings of the 7th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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Checkpointing, i.e., recording the volatile state of a virtual machine (VM) running as a guest in a virtual machine monitor (VMM) for later restoration, includes storing the memory available to the VM. Typically, a full image of the VM's memory along with processor and device states are recorded. With guest memory sizes of up to several gigabytes, the size of the checkpoint images becomes more and more of a concern.In this work we present a technique for fast and space-efficient checkpointing of virtual machines. In contrast to existing methods, our technique eliminates redundant data and stores only a subset of the VM's memory pages. Our technique transparently tracks I/O operations of the guest to external storage and maintains a list of memory pages whose contents are duplicated on non-volatile storage. At a checkpoint, these pages are excluded from the checkpoint image.We have implemented the proposed technique for paravirtualized as well as fully-virtualized guests in the Xen VMM. Our experiments with a paravirtualized guest (Linux) and two fullyvirtualized guests (Linux, Windows) show a significant reduction in the size of the checkpoint image as well as the time required to complete the checkpoint. Compared to the current Xen implementation, we achieve, on average, an 81% reduction in the stored data and a 74% reduction in the time required to take a checkpoint for the paravirtualized Linux guest. In a fully-virtualized environment running Windows and Linux guests, we achieve a 64% reduction of the image size along with a 62% reduction in checkpointing time.

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Parallax

Cited by 71 publications

References 7 publications

Multi-layered Virtual Machines for Security Updates in Grid Environments

Multi-layered Virtual Machines for Security Updates in Grid Environments

GPU Virtualization using PCI Direct Pass-Through

Fast and space-efficient virtual machine checkpointing

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