An updated performance comparison of virtual machines and Linux containers

Felter, Wes; Ferreira, Alexandre; Rajamony, Ram; Rubio, J.

doi:10.1109/ispass.2015.7095802

Cited by 834 publications

(471 citation statements)

References 22 publications

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“…Existing empirical studies related specifically to Docker typically focus on performance aspects, often comparing container performance and overhead with traditional virtualization techniques [22], [23], [24]. However, despite this shortage of empirical work, the importance of Docker for academia and industry is rarely doubted in literature.…”

Section: Related Workmentioning

confidence: 99%

An empirical analysis of the Docker container ecosystem on GitHub

Cito

Schermann

Wittern

et al. 2017

Preprint

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Abstract-Docker allows packaging an application with its dependencies into a standardized, self-contained unit (a so-called container), which can be used for software development and to run the application on any system. Dockerfiles are declarative definitions of an environment that aim to enable reproducible builds of the container. They can often be found in source code repositories and enable the hosted software to come to life in its execution environment. We conduct an exploratory empirical study with the goal of characterizing the Docker ecosystem, prevalent quality issues, and the evolution of Dockerfiles. We base our study on a data set of over 70000 Dockerfiles, and contrast this general population with samplings that contain the Top-100 and Top-1000 most popular Docker-using projects. We find that most quality issues (28.6%) arise from missing version pinning (i.e., specifying a concrete version for dependencies). Further, we were not able to build 34% of Dockerfiles from a representative sample of 560 projects. Integrating quality checks, e.g., to issue version pinning warnings, into the container build process could result into more reproducible builds. The most popular projects change more often than the rest of the Docker population, with 5.81 revisions per year and 5 lines of code changed on average. Most changes deal with dependencies, that are currently stored in a rather unstructured manner. We propose to introduce an abstraction that, for instance, could deal with the intricacies of different package managers and could improve migration to more light-weight images.

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Section: Related Workmentioning

confidence: 99%

An empirical analysis of the Docker container ecosystem on GitHub

Cito

Schermann

Wittern

et al. 2017

Preprint

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show abstract

“…Container virtualisation has proved to be a powerful tool to achieve portability of a code and it's execution environment, through runtimes such as Docker, LXC, Singularity and others -without the performance cost of traditional Virtual Machines (Chamberlain, Invenshure, and Schommer 2014;Felter et al 2014). …”

Section: Resultsmentioning

confidence: 99%

VCC: A framework for building containerized reproducible cluster software environments

Higgins¹,

Holmes²,

Venters³

2017

JOSS

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SummaryThe problem of portability and reproducibility of the software used to conduct computational experiments has recently come to the fore. Container virtualisation has proved to be a powerful tool to achieve portability of a code and it's execution environment, through runtimes such as Docker, LXC, Singularity and others -without the performance cost of traditional Virtual Machines (Chamberlain, Invenshure, and Schommer 2014;Felter et al. 2014).However, scientific software often depends on a system foundation that provides middleware, libraries, and other supporting software in order for the code to execute as intended. Typically, container virtualisation addresses only the portability of the code itself, which does not make it inherently reproducible. For example, a containerized MPI application may offer binary compatibility between different systems, but for execution as intended, it must be run on an existing cluster that provides the correct interfaces for parallel MPI execution.As a greater demand to accomodate a diverse range of disciplines is placed on high performance and cluster resources, the ability to quickly create and teardown reproducible, transitory virtual environments that are tailored for an individual task or experiment will be essential.The Virtual Container Cluster (VCC) is a framework for building containers that achieve this goal, by encapsulating a parallel application along with an execution model, through a set of dependency linked services and built-in process orchestration. This promotes a high degree of portability, and offers easier reproducibility by shipping the application along with the foundation required to execute it -whether that be an MPI cluster, big data processing framework, bioinformatics pipeline, or any other execution model (Higgins, Holmes, and Venters 2017).

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“…is increases I/O latency and reduces available CPU cycles for the entire system [21]. Second, the container results in frequent page faults (13 page faults/s) compared to the native Linux (2 page faults/s) because of the limited memory capacity of containers (1 GB in our experiments).…”

Section: Performance Comparison With Host Osmentioning

confidence: 88%

The Impact of Container Virtualization on Network Performance of IoT Devices

Lee

Kim

2018

Mobile Information Systems

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Container-based virtualization offers advantages such as high performance, resource efficiency, and agile environment. ese advantages make Internet of ings (IoT) device management easy. Although container-based virtualization has already been introduced to IoT devices, the different network modes of containers and their performance issues have not been addressed. Since the network performance is an important factor in IoT, the analysis of the container network performance is essential. In this study, we analyze the network performance of containers on an IoT device, Raspberry Pi 3. e results show that the network performance of containers is lower than that of the native Linux, with an average performance difference of 6% and 18% for TCP and UDP, respectively. In addition, the network performance of containers varies depending on the network mode. When a single container runs, bridge mode achieves higher performance than host mode by 25% while host mode shows better performance than bridge mode by 45% in the multicontainer environment.

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An updated performance comparison of virtual machines and Linux containers

Cited by 834 publications

References 22 publications

An empirical analysis of the Docker container ecosystem on GitHub

An empirical analysis of the Docker container ecosystem on GitHub

VCC: A framework for building containerized reproducible cluster software environments

The Impact of Container Virtualization on Network Performance of IoT Devices

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