Bringing the ATLAS HammerCloud setup to the next level with containerization

Author:

Rottler Benjamin,Böhler Michael,Duckeck Günter,Lory Alexander,Mitterer Christoph Anton,Schovancova Jaroslava

Abstract

HammerCloud (HC) is a testing service and framework for continuous functional tests, on-demand large-scale stress tests, and performance benchmarks. It checks the computing resources and various components of distributed systems with realistic full-chain experiment workflows. The HammerCloud software was initially developed in Python 2. After support for Python 2 was discontinued in 2020, migration to Python 3 became vital in order to fulfill the latest security standards and to use the new CERN Single Sign-On, which requires Python 3. The current deployment setup based on RPMs allowed a stable deployment and secure maintenance over several years of operations for the ATLAS and CMS experiments. However, the current model is not flexible enough to support an agile and rapid development process. Therefore, we have decided to use a containerization solution, and switched to industry-standard technologies and processes. Having an “easy to spawn” instance of HC enables a more agile development cycle and easier deployment. With the help of such a containerized setup, CI/CD pipelines can be integrated into the automation process as an extra layer of verification. A quick onboarding process for new team members and communities is essential, as there is a lot of personnel rotation and a general lack of personpower. This is achieved with the container-based setup, as developers can now work locally with a quick turnaround without needing to set up a production-like environment first. These developments empower the whole community to test and prototype new ideas and deliver new types of resources or workflows to our community.

Publisher

EDP Sciences

Reference13 articles.

1. WLCG, https://wlcg.web.cern.ch, accessed 6th July 2023

2. PanDA for ATLAS distributed computing in the next decade

3. HEPiX, HEPScore23, https://w3.hepix.org/benchmarking.html, accessed 6th July 2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3