Scalable virtual machine deployment using VM image caches

Razavi, Kaveh; Kielmann, Thilo

doi:10.1145/2503210.2503274

Cited by 44 publications

(38 citation statements)

References 23 publications

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“…We used the InfiniBand network for our experiments to avoid the network bottlenecks. In situations where premium networks are not available, low-overhead caching techniques [24,23] avoid the network bottlenecks associated with VM deployment. The head node runs an off-the-shelf NFS server optimized for read and write requests at QCOW2's cluster size [10], which stores the VM disk images.…”

Section: Methodsmentioning

confidence: 99%

“…To avoid slow RPC requests/responses that execute serially, we changed the interface to allow for multiple deployment requests to be sent with a single RPC call. total VM deployment time is the booting of the guest OS that can be further improved as suggested in [24,26].…”

Section: Serial Deploymentsmentioning

confidence: 99%

“…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. µVM/Squirrel [26] caches minimal memory snapshots and employs fast VM resume, and resource hot-plugging for instant and scalable VM startup.…”

Section: Scalable Transfer/caching Of Vm Imagesmentioning

confidence: 99%

“…and the actual VM boot time, including the transfer of the VM image to the selected compute node. State-of-the-art solutions tackle both problems through a variety of architectural and algorithmic approaches [4,20,24]. In this paper, we focus on the first problem, with a different approach from the state of the art.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scaling VM Deployment in an Open Source Cloud Stack

Razavi

Costache

Gardiman

et al. 2015

Proceedings of the 6th Workshop on Scientific Cloud Computing

Self Cite

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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: Methodsmentioning

confidence: 99%

Section: Serial Deploymentsmentioning

confidence: 99%

Section: Scalable Transfer/caching Of Vm Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scaling VM Deployment in an Open Source Cloud Stack

Razavi

Costache

Gardiman

et al. 2015

Proceedings of the 6th Workshop on Scientific Cloud Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each private virtual disk of VM is an incremental image file to the base image and be allocated by the strategy of copy on write (COW). According to the documented details of QCOW2 [17], VM's write request, excluding rewrite one, appends new data to the end of an incremental image file. In doing so, VM's physical disk data can be allocated on the demand of end user.…”

Section: Shadow-basementioning

confidence: 99%

Triple-L: Improving CPS Disk I/O Performance in a Virtualized NAS Environment

Dong

Tang

et al. 2017

IEEE Systems Journal

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Abstract-Network-attached storage (NAS) provides cyber-physical systems (CPS) with the scalable, efficient and reliable backing storage, such as the mobile virtual desktop based on cloud infrastructure. Within this storage architecture, virtual machine instances (VMs) running in the NAS client usually receive data from the complex physical world and then persist them in the neat cyberspace in NAS sever. In this paper, we propose Triple-L to improve VM disk I/O performance in the NAS architecture. According to the specific storage semantic, Triple-L decouples VM image file into several sub-files at the host layer, and then selectively moves them into NAS client. In such a way, a VM disk I/O request may be proceeded locally in NAS client, instead of walking the external networking path repetitively between NAS server and client. We have implemented Triple-L in a Xen-based NAS system. An accessory solution for dealing with storage failure as well as VM live migration on Triple-L is also discussed and evaluated. The experimental result shows that our work can effectively improve the disk I/O performance of VMs. Meanwhile, it brings moderate overhead for VM live migration.Index Terms-Cyber-physical systems, virtualization, disk I/O, network-attached storage, image file.

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Semantic approach for multi‐objective optimisation of the ENTICE distributed Virtual Machine and container images repository

Gec

Kimovski

Paščinski

et al. 2017

Concurrency and Computation

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New software engineering technologies facilitate development of applications from reusable software components, such as Virtual Machine and container images (VMI/CIs). Key requirements for the storage of VMI/CIs in public or private repositories are their fast delivery and cloud deployment times. ENTICE is a federated storage facility for VMI/CIs that provides optimisation mechanisms through the use of fragmentation and replication of images and a Pareto Multi-Objective Optimisation (MO) solver. The operation of the MO solver is, however, time-consuming due to the size and complexity of the metadata, * Vlado Stankovski, University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, SI-1000 Ljubljana, Slovenia.2 E-mail: vlado.stankovski@fgg.uni-lj.si 1 specifying various non-functional requirements for the management of VMI/CIs, such as geolocation, operational cost and delivery time. In this work, we address this problem with a new semantic approach, which uses an ontology of the federated ENTICE repository, knowledge base and constraint-based reasoning mechanism. Open Source technologies such as Protégé, Jena Fuseki and Pellet were used to develop a solution. Two specific use cases: (1) repository optimisation with offline and (2) online redistribution of VMI/CIs, are presented in detail. In both use cases, data from the knowledge base is provided to the MO solver. It is shown that Pellet based reasoning can be used to reduce the input metadata size used in the optimisation process by taking into consideration the geographic location of the VMI/CIs and the provenance of the VMI fragments. It is shown that this process leads to reduction of the input metadata size for the MO solver by up to 60% and reduction of the total optimization time of the MO solver by up to 68%, while fully preserving the quality of the solution, which is significant.

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Scalable virtual machine deployment using VM image caches

Cited by 44 publications

References 23 publications

Scaling VM Deployment in an Open Source Cloud Stack

Scaling VM Deployment in an Open Source Cloud Stack

Triple-L: Improving CPS Disk I/O Performance in a Virtualized NAS Environment

Semantic approach for multi‐objective optimisation of the ENTICE distributed Virtual Machine and container images repository

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