VDN: Virtual machine image distribution network for cloud data centers

Peng, Chunyi; Kim, Minkyong; Zhang, Zhe; Liu, Hui

doi:10.1109/infcom.2012.6195556

Cited by 120 publications

(81 citation statements)

References 17 publications

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“…Liquid [33] and similar systems (e.g., [13,19,32]) are designed for scalable VM image distribution. Squirrel's cVolumes [23] persistently cache all the blocks needed for starting different VMs by capturing their boot working sets [24] in a compressed ZFS file system.…”

Section: Scalable Transfer/caching Of Vm Imagesmentioning

confidence: 99%

Scaling VM Deployment in an Open Source Cloud Stack

Razavi

Costache

Gardiman

et al. 2015

Proceedings of the 6th Workshop on Scientific Cloud Computing

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Interactive High Performance Computing (HPC) workloads take advantage of the elasticity of clouds to scale their computation based on user demand by dynamically provisioning virtual machines during their runtime. As in this case users require the results of their computation in a short time, the time to start the provisioned virtual instances becomes crucial. In this paper we study the deployment scalability of OpenNebula, an open-source cloud stack, with respect to these workloads. We describe our efforts for tuning the infrastructure's and OpenNebula's configuration as well as solving scalability issues in its implementation. After tuning both infrastructure and cloud stack, the simultaneous deployment of 512 VMs improved by 5.9× on average, from 615 to 104 seconds, and after optimizing the implementation, the deployment time improved by 12× on average, to 53.54 seconds. These results suggest two possible improvement opportunities that can be useful for both cloud developers and scientific users deploying a cloud stack to avoid such scalability issues in the future. First, the tuning process of a cloud stack can be improved through automatic tools that adapt the configuration to the workload and infrastructure characteristics. Second, the code scalability issues can be avoided through a testing infrastructure that supports large scale emulation.

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Section: Scalable Transfer/caching Of Vm Imagesmentioning

confidence: 99%

Scaling VM Deployment in an Open Source Cloud Stack

Razavi

Costache

Gardiman

et al. 2015

Proceedings of the 6th Workshop on Scientific Cloud Computing

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“…Schmidt et al use Unionfs [18], a sophisticated, layered file system along with multicasting to minimize network transfer times of VMIs. VDN [14] is a network hierarchyaware system for transferring VMIs to compute nodes in units of small chunks that can be retrieved from local neighbors.…”

Section: Efficient Vmi Transfermentioning

confidence: 99%

Reducing VM Startup Time and Storage Costs by VM Image Content Consolidation

Razavi

Razorea

Kielmann

2014

Euro-Par 2013: Parallel Processing Workshops

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Abstract. Elastic cloud applications rely on fast virtual machine (VM) startup, e.g. when scaling out for handling increased workload. While there have been recent studies into the VM startup time in clouds, the effects of the VM image (VMI) disk size and its contents are little understood. To fill this gap, we present a detailed study of these factors on Amazon EC2. Based on our findings, we developed a novel approach for consolidating size and contents of VMIs. We then evaluated our approach with the ConPaaS VMI, an open-source Platform-as-a-Service runtime. Compared to an unmodified ConPaaS VMI, our approach results in up to four times reduction of the disk size, three times speedup for the VM startup time, and three times reduction of storage cost.

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“…VDN [22] is a network hierarchy-aware system for transferring VMIs to compute nodes. Each VMI is divided into chunks and each compute node has a cache for these chunks.…”

Section: Many Vmismentioning

confidence: 99%

Scalable virtual machine deployment using VM image caches

Razavi¹,

Kielmann²

2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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In IaaS clouds, VM startup times are frequently perceived as slow, negatively impacting both dynamic scaling of web applications and the startup of high-performance computing applications consisting of many VM nodes. A significant part of the startup time is due to the large transfers of VM image content from a storage node to the actual compute nodes, even when copy-on-write schemes are used. We have observed that only a tiny part of the VM image is needed for the VM to be able to start up. Based on this observation, we propose using small caches for VM images to overcome the VM startup bottlenecks. We have implemented such caches as an extension to KVM/QEMU. Our evaluation with up to 64 VMs shows that using our caches reduces the time needed for simultaneous VM startups to the one of a single VM.

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VDN: Virtual machine image distribution network for cloud data centers

Cited by 120 publications

References 17 publications

Scaling VM Deployment in an Open Source Cloud Stack

Scaling VM Deployment in an Open Source Cloud Stack

Reducing VM Startup Time and Storage Costs by VM Image Content Consolidation

Scalable virtual machine deployment using VM image caches

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