research-article

Evaluation of Docker Containers for Scientific Workloads in the Cloud

Authors:

Pankaj Saha,

Angel Beltre,

Piotr Uminski,

Madhusudhan GovindarajuAuthors Info & Claims

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

Article No.: 11, Pages 1 - 8

https://doi.org/10.1145/3219104.3229280

Published: 22 July 2018 Publication History

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Abstract

The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment.

This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mechanism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal resource management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container technologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that scientific workloads for both Docker and Singularity based containers can achieve near-native performance.

Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation. Both Docker and Singularity make it possible to directly use the underlying network fabric from the containers for coarsegrained resource allocation.

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Evaluation of Docker Containers for Scientific Workloads in the Cloud

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Information & Contributors

Information

Published In

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

July 2018

652 pages

ISBN:9781450364461

DOI:10.1145/3219104

General Chair:
Sergiu Sanielevici
Pittsburgh Supercomputing Center

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2018

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PEARC '18

PEARC '18: Practice and Experience in Advanced Research Computing

July 22 - 26, 2018

PA, Pittsburgh, USA

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PEARC '18 Paper Acceptance Rate 79 of 123 submissions, 64%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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Patrice LSinde RLeo J(2024)A Novel Mechanism for Detection of Address Resolution Protocol Spoofing Attacks in Large-Scale Software-Defined NetworksIEEE Access10.1109/ACCESS.2024.340967912(80255-80265)Online publication date: 2024
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