Performance Comparison Analysis of Linux Container and Virtual Machine for Building Cloud

Seo, Kyoung-Taek; Hwang, Hyunseo; Moon, Il-Young; Kwon, Oh-Young; Kim, Byeong-Jun

doi:10.14257/astl.2014.66.25

Cited by 98 publications

(43 citation statements)

References 4 publications

(4 reference statements)

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“…Felter et al [12] also looked at the performance differential of non-scientific software within virtualised and containerised environments. The performance studies published in the literature [10,12,24] found negligible differences between container and native performance, as long as the container is setup and used appropriately, e.g. using data mounts for I/O intensive tasks [12] and not setting up many containers for individual tasks with run times on the order of container setup time (typically fractions of a second) [10].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, instead of concentrating on how containers aid scientific reproducibility [7,9,14], we consider how containers can be used to ease the difficulty of sharing and distributing scientific code to developers and end-users on a wide range of computing platforms. Secondly, unlike previous studies [10,12,24] that showed performance parity between containerised and native shared memory parallel software, we consider performance of software using distributed memory parallel programming models (e.g. MPI).…”

Section: Introductionmentioning

confidence: 99%

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Containers for Portable, Productive, and Performant Scientific Computing

Hale

Richardson

et al. 2017

Comput. Sci. Eng.

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Containers are an emerging technology that hold promise for improving productivity and code portability in scientific computing. We examine Linux container technology for the distribution of a non-trivial scientific computing software stack and its execution on a spectrum of platforms from laptop computers through to high performance computing (HPC) systems. We show on a workstation and a leadership-class HPC system that when deployed appropriately there are no performance penalties running scientific programs inside containers. For Python code run on large parallel computers, the run time is reduced inside a container due to faster library imports. The software distribution approach and data that we present will help developers and users decide on whether container technology is appropriate for them. We also provide guidance for the vendors of HPC systems that rely on proprietary libraries for performance on what they can do to make containers work seamlessly and without performance penalty.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Containers for Portable, Productive, and Performant Scientific Computing

Hale

Richardson

et al. 2017

Comput. Sci. Eng.

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“…In 2014, Seo et al [8] compared containers to VMs in terms of disk utilization, boot time and operation speed. To compare both disk utilization, authors used a service generating several files, and replicated this service using KVM and Dockers.…”

Section: Related Workmentioning

confidence: 99%

Comparative experimental analysis of the quality-of-service and energy-efficiency of VMs and containers' consolidation for cloud applications

Cuadrado-Cordero

Orgerie

Menaud

2017

2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Abstract-The consolidation of services is a widely accepted technique for IaaS Cloud providers to reducing energy consumption and improving the utilization of their resources. This technique is based on distributing all services in the minimum amount of servers. This way, the overall energy consumption of the datacenter is reduced, as less servers are needed to be active. Traditionally, research has focused on strategies for consolidation of Virtual Machines (VMs), but containers are changing the landscape of Cloud services. Containers are expected to optimize the consolidation of services by reducing the amount of needed resources, thus allocating more services using less servers. However, while multiple research works have been produced in the Energy Efficiency (EE) achieved through consolidation of VMs, there is yet no experimental work on how consolidation of containers affects EE, when assuming a given Quality-of-Service (QoS) to the user. In this paper we show an experimental analysis on the effects of consolidation of containers in the QoS and EE, compared to the consolidation of VMs. We demonstrate that the consolidation of containers is indeed more optimal than the one of VMs, both in terms of QoS and EE. Consecutively, we analyze how the degradation of the service is produced both in QoS and EE, and we show how QoS is the variable which is more affected by consolidation. This work provides the necessary scientific background on consolidation of two widely used virtualization technologies, and we believe it is useful for future works on the optimization of resources in datacenters.

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“…[14] only compares performance of three types of servers with fixed one server. The papers of Seo et al [17] and Morabito et al [18] also compare performances of two or three types of servers by several benchmarks. In this section, we confirm server performance and start-up time when number of servers is changed.…”

Section: Problems Of Multiple Types Of Iaas Server Provisioningmentioning

confidence: 99%

Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

Yamato

2016

SOCA

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In this paper, we propose a server architecture recommendation and automatic performance verification technology, which recommends and verifies appropriate server architecture on Infrastructure as a Service (IaaS) cloud with bare metal servers, container-based virtual servers and virtual machines. Recently, cloud services are spread, and providers provide not only virtual machines but also bare metal servers and container-based virtual servers. However, users need to design appropriate server architecture for their requirements based on three types of server performances, and users need much technical knowledge to optimize their system performance. Therefore, we study a technology that satisfies users' performance requirements on these three types of IaaS cloud. Firstly, we measure performance and start-up time of a bare metal server, Docker containers, KVM (Kernelbased Virtual Machine) virtual machines on OpenStack with changing number of virtual servers. Secondly, we propose a server architecture recommendation technology based on the measured quantitative data. A server architecture recommendation technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology that executes necessary performance tests automatically on provisioned user environments according to the template. We implement proposed technologies, confirm performance and show the effectiveness. B Yoji Yamato

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Performance Comparison Analysis of Linux Container and Virtual Machine for Building Cloud

Cited by 98 publications

References 4 publications

Containers for Portable, Productive, and Performant Scientific Computing

Containers for Portable, Productive, and Performant Scientific Computing

Comparative experimental analysis of the quality-of-service and energy-efficiency of VMs and containers' consolidation for cloud applications

Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

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