Configuration Management with Puppet

Ali, Syed

doi:10.1007/978-1-4842-0511-2_5

Cited by 2 publications

(3 citation statements)

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“…First of all, every EOS server needs to be configured in order to deliver the service they are meant for. The entire configuration of the EOS clusters relies on CERN's puppet infrastructure [16]. All the needed configuration files are stored in a gitlab repository, which the operators can modify and easily deploy thanks to Puppet.…”

Section: Operating Eosmentioning

confidence: 99%

Operation of the CERN disk storage infrastructure during LHC Run-3

Caffy,

Amadio,

Arsuaga Rios

et al. 2024

EPJ Web of Conf.

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The CERN IT Storage group operates multiple distributed storage systems to support all CERN data storage requirements. The storage and distribution of physics data generated by LHC and non-LHC experiments is one of the biggest challenges the group has to take on during LHC Run-3.EOS [1], the CERN distributed disk storage system is playing a key role in LHC data-taking. During the first ten months of 2022, more than 440PB have been written by the experiments and 2.9EB have been read out. The data storage requirements of LHC Run-3 are higher than what was previously delivered. The storage operations team has started investigating multiple areas to upgrade and optimize the current storage resources. A new, dedicated and redundant EOS infrastructure based on 100Gbit servers was installed, commissioned and deployed for the ALICE Online and Offline (O2) project. This cluster can sustain high-throughput data transfer between the ALICE Event Processing Nodes (EPN) and the CERN’s data center.This paper will present the architecture, techniques and workflows in place allowing EOS to deliver fast, reliable and scalable data storage to meet experiment needs during LHC Run-3 and beyond.

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Section: Operating Eosmentioning

confidence: 99%

Operation of the CERN disk storage infrastructure during LHC Run-3

Caffy,

Amadio,

Arsuaga Rios

et al. 2024

EPJ Web of Conf.

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“…Then, she moves to a directory named catalogue-service, add a configuration file located on her filesystem and the javascript source code into this directory, and finally starts the nodejs environement to host her service. It is a good practice to publish the descriptor associated to a given image on the repository, and tools exist to decompile an image and retrieve the essential instructions used to build it 1 .…”

Section: Listing 1: Product Catalogue Descriptormentioning

confidence: 99%

“…Automated approaches have been proposed to support the scripting (using both declarative [10] or imperative [7] descriptors) of distributed deployments, for example using dedicated architecture deployment languages [11]. These academic approaches were complemented by industrial implementations during the rise of the cloud era, with systems such as Chef [15] or Puppet [1], to orchestrate different sequences of shell commands to support distributed deployment. These systems also propose a black-box reuse mechanism that can lead to guideline violation or optimization issue.…”

Section: Related Workmentioning

confidence: 99%

Supporting micro-services deployment in a safer way

Benni

Mosser

Collet

et al. 2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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The SOA ecosystem has drastically evolved since its childhood in the early 2000s. From monolithic services, micro-services now cooperate together in ultra-large scale systems. In this context, there is a tremendous need to deploy frequently new services, or new version of existing services. Container-based technologies (e.g., Docker) emerged recently to tool such deployments, promoting a black-box reuse mechanism to support off-the-shelf deployments. Unfortunately, from the service deployment point of view, such form of black-box reuse prevent to ensure what is really shipped inside the container with the service to deploy. In this paper, we propose a formalism to model and statically analyze service deployment artifacts based on state of the art deployment platforms. The static analysis mechanism leverages the hierarchy of deployment descriptors to verify a given deployment, as well as rewrite it to automatically fix common errors. The approach is validated through the automation of the guidelines provided by the user community associated to the reference Docker engine, and the analysis of 20,000 real deployment descriptors (hosted on GitHub).

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Configuration Management with Puppet

Cited by 2 publications

References 0 publications

Operation of the CERN disk storage infrastructure during LHC Run-3

Operation of the CERN disk storage infrastructure during LHC Run-3

Supporting micro-services deployment in a safer way

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