6,703 research outputs found
End-to-End Automation in Cloud Infrastructure Provisioning
Infrastructure provisioning in the cloud can be time-consuming and error-prone due to the manual process of building scripts. Configuration Management Tools (CMT) such as Ansible, Puppet or Chef use scripts to orchestrate the infrastructure provisioning and its configuration in the cloud. Although CMTs have a high level of automation in the infrastructure provisioning still remains a challenge to automate the iterative development process in the cloud. Infrastructure as Code is a process where the infrastructure is automatically built, managed, and provisioned by scripts. However, there are several infrastructure provisioning tools and scripting languages that need to be used coherently. In previous work, we have introduced the ARGON modelling tool with the purpose of abstracting the complexity of working with different DevOps tools through a DSL. In this work, we present an end-to- end automation for a toolchain for infrastructure provisioning in the cloud based on DevOps community tools and ARGON
Academic Cloud Computing Research: Five Pitfalls and Five Opportunities
This discussion paper argues that there are five fundamental pitfalls, which
can restrict academics from conducting cloud computing research at the
infrastructure level, which is currently where the vast majority of academic
research lies. Instead academics should be conducting higher risk research, in
order to gain understanding and open up entirely new areas.
We call for a renewed mindset and argue that academic research should focus
less upon physical infrastructure and embrace the abstractions provided by
clouds through five opportunities: user driven research, new programming
models, PaaS environments, and improved tools to support elasticity and
large-scale debugging. The objective of this paper is to foster discussion, and
to define a roadmap forward, which will allow academia to make longer-term
impacts to the cloud computing community.Comment: Accepted and presented at the 6th USENIX Workshop on Hot Topics in
Cloud Computing (HotCloud'14
Automation of the Continuous Integration (CI) - Continuous Delivery/Deployment (CD) Software Development
Continuous Integration (CI) is a practice in software development where developers periodically merge code changes in a central shared repository, after which automatic versions and tests are executed. CI entails an automation component (the target of this project) and a cultural one, as developers have to learn to integrate code periodically. The main goal of CI is to reduce the time to feedback over the software integration process, allowing to locate and fix bugs more easily and quickly, thus enhancing it quality while reducing the time to validate and publish new soIn traditional software development, where teams of developers worked on the same project in isolation, often led to problems integrating the resulting code. Due to this isolation, the project was not deliverable until the integration of all its parts, which was tedious and generated errors. The Continuous Integration (CI ) emerged as a practice to solve the problems of traditional methodology, with the aim of improving the quality of the code. This thesis sets out what is it and how Continuous Integration is achieved, the principles that makes it as effective as possible and the processes that follow as a consequence, to thus introduce the context of its objective: the creation of a system that automates the start-up and set-up of an environment to be able to apply the methodology of continuous integration
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
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