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Two-stage performance engineering of container-based virtualization

Li, Zheng; Kihl, Maria LU ; Chen, Yiqun and Zhang, He (2018) In Advances in Science, Technology and Engineering Systems 3(1). p.521-536
Abstract

Cloud computing has become a compelling paradigm built on compute and storage virtualization technologies. The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Given the recent booming of the container ecosystem, the container-based virtualization starts receiving more attention for being a promising alternative. Although the container technologies are generally considered to be lightweight, no virtualization solution is ideally resource-free, and the corresponding performance overheads will lead to negative impacts on the quality of Cloud services. To facilitate understanding container technologies from the performance engineering's perspective, we conducted two-stage performance... (More)

Cloud computing has become a compelling paradigm built on compute and storage virtualization technologies. The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Given the recent booming of the container ecosystem, the container-based virtualization starts receiving more attention for being a promising alternative. Although the container technologies are generally considered to be lightweight, no virtualization solution is ideally resource-free, and the corresponding performance overheads will lead to negative impacts on the quality of Cloud services. To facilitate understanding container technologies from the performance engineering's perspective, we conducted two-stage performance investigations into Docker containers as a concrete example. At the first stage, we used a physical machine with “just-enough” resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Moreover, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed. At the ongoing second stage, we employed a physical machine with “fair-enough” resource to implement a container-based MapReduce application and try to optimize its performance. In fact, this machine failed in affording VM-based MapReduce clusters in the same scale. The performance tuning results show that the effects of different optimization strategies could largely be related to the data characteristics. For example, LZO compression can bring the most significant performance improvement when dealing with text data in our case.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cloud Computing, Container, Hypervisor, MapReduce, Performance Engineering, Virtualization
in
Advances in Science, Technology and Engineering Systems
volume
3
issue
1
pages
16 pages
publisher
ASTES Publishers
external identifiers
  • scopus:85061737092
DOI
10.25046/aj030163
language
English
LU publication?
yes
id
da6a2d4c-fa66-4730-9830-e0e344f23d72
date added to LUP
2019-03-05 13:05:08
date last changed
2019-03-27 04:39:56
@article{da6a2d4c-fa66-4730-9830-e0e344f23d72,
  abstract     = {<p>Cloud computing has become a compelling paradigm built on compute and storage virtualization technologies. The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Given the recent booming of the container ecosystem, the container-based virtualization starts receiving more attention for being a promising alternative. Although the container technologies are generally considered to be lightweight, no virtualization solution is ideally resource-free, and the corresponding performance overheads will lead to negative impacts on the quality of Cloud services. To facilitate understanding container technologies from the performance engineering's perspective, we conducted two-stage performance investigations into Docker containers as a concrete example. At the first stage, we used a physical machine with “just-enough” resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Moreover, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed. At the ongoing second stage, we employed a physical machine with “fair-enough” resource to implement a container-based MapReduce application and try to optimize its performance. In fact, this machine failed in affording VM-based MapReduce clusters in the same scale. The performance tuning results show that the effects of different optimization strategies could largely be related to the data characteristics. For example, LZO compression can bring the most significant performance improvement when dealing with text data in our case.</p>},
  author       = {Li, Zheng and Kihl, Maria and Chen, Yiqun and Zhang, He},
  keyword      = {Cloud Computing,Container,Hypervisor,MapReduce,Performance Engineering,Virtualization},
  language     = {eng},
  number       = {1},
  pages        = {521--536},
  publisher    = {ASTES Publishers},
  series       = {Advances in Science, Technology and Engineering Systems},
  title        = {Two-stage performance engineering of container-based virtualization},
  url          = {http://dx.doi.org/10.25046/aj030163},
  volume       = {3},
  year         = {2018},
}