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

Razavi, Kaveh; Razorea, Liviu Mihai; Kielmann, Thilo

doi:10.1007/978-3-642-54420-0_8

Cited by 17 publications

(16 citation statements)

References 12 publications

(14 reference statements)

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“…VM Image Creation/VM Image Search: The user request may either require the creation of a new VM image or may be satisfied by reusing an existing image. Krsul et al [2004] and Razavi et al [2014b] propose ways to speed up the creation of a new VM image from a set of software requirements specified by a client. In this article, we focus on the other scenario wherein the user requests the reuse of an existing image.…”

Section: The Deployment Processmentioning

confidence: 99%

Handling Boot Storms in Virtualized Data Centers—A Survey

Samant

Bellur

2016

ACM Comput. Surv.

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Large-scale virtual machine (VM) deployment in virtualized data centers is a very slow process. This is primarily due to the resource bottlenecks that are created at the storage, network, and host physical machines when a large number of VMs are requested simultaneously. For companies that provide virtual desktops to their employees, it is common to encounter such requests each day, when their employees turn up for work. In addition, a large number of VMs are often required to be deployed instantly, in order to absorb a spike in the workload, at online e-commerce websites. In such scenarios, long deployment times are unacceptable, and reducing them is of paramount importance. In this article, we first abstract out the key techniques suggested in the literature to speed up this deployment process. We follow this with a classification of these techniques into a taxonomy and propose a framework that can be used to compare them. Finally, we identify problem areas that warrant further research and bring out the shortcomings of the current state-of-the-art solutions.

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Section: The Deployment Processmentioning

confidence: 99%

Handling Boot Storms in Virtualized Data Centers—A Survey

Samant

Bellur

2016

ACM Comput. Surv.

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“…Delivery of VMIs from a VMI repository to the actual computing infrastructure strongly affects the start‐up time of VMs. This process is considered to be slow and negatively impacting the dynamic scaling of cloud applications . This problem has been generally addressed with the development of VMI/CI distribution networks aiming to speed up VMI/CI speed up and delivery, which implement various degrees of sophistication.…”

Section: Related Workmentioning

confidence: 99%

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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“…To minimize the disk size, they estimate the installation size of each package. [25] follows a similar approach, but it uses a fresh VM image for copying the packages, resulting in clean VM images without a need for size estimations.…”

Section: Vm Image Consolidationmentioning

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

Cited by 17 publications

References 12 publications

Handling Boot Storms in Virtualized Data Centers—A Survey

Handling Boot Storms in Virtualized Data Centers—A Survey

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

Scaling VM Deployment in an Open Source Cloud Stack

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