Virtualization Costs: Benchmarking Containers and Virtual Machines Against Bare-Metal

International audienceDevOps advocates the usage of Virtualization Technologies (VT), such as Virtual Machines and Containers. However, it is complex to predict how the usage of a given VT will impact on the performance of an application. In this paper, we present a collection of reference benchmarks that developers can use to orient when looking for the best-performing VT w.r.t their application profile. To gather our benchmarks in a resource-wise comprehensive and comparable way, we introduce VTmark: a semi-automatic open-source suite that assembles off-the-shelf tools for benchmarking the different resources used by applications (CPU, RAM, etc.). After performing a survey of VTs in the market, we use VTmark to report the benchmarks of 6 of the most widely adopted and popular ones, namely Docker, KVM, Podman, VMWare Workstation, VirtualBox, and Xen. To validate the accuracy of our reference benchmarks, we show how they correlate with the profile performance of a production-grade application ported and deployed on the considered VTs. Beyond our immediate results, VTmark let us shed light on some contradicting findings in the related literature and, by releasing VTmark , we provide DevOps with an open-source, extendable tool to assess the (resource-wise) costs of VTs

CERN openlab Technical Workshop

1. Data Analytics for Industrial Control Systems 2. An update on the Next Generation Archiver projec

Machine Learning in High Energy Physics Community White Paper

peer reviewedMachine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit