Progetto CloudVeneto.it - Status Report

Dalla fine del 2015 è attiva e a disposizione degli utenti dell’Università di Padova una infrastruttura di calcolo chiamata CloudVeneto.it. Si tratta di un servizio Cloud di tipo IaaS (Infrastructure as a Service), che utilizza il middleware OpenStack ed è stata realizzata mettendo a frutto finanziamenti universitari e l’esperienza acquisita negli ultimi anni dal gruppo Cloud dell’INFN di Padova e Legnaro. CloudVeneto.it permette agli utenti di istanziare con pochi comandi le Virtual Machine che possono poi essere utilizzate per le proprie esigenze di calcolo. Le Virtual Machine possono essere impiegate in diversi modi, permettendo ad esempio: ● l’esecuzione di applicazioni interattive, ● il dispiegamento di servizi accessibili da remoto, ● la configurazione di cluster per l’esecuzione di istanze multiple di applicazioni di calcolo in modalità batch, ● l’esecuzione di applicazioni di calcolo parallelo che richiedono l’impiego simultaneo di più macchine virtuali. Per l’utente diventa così possibile configurare all’occorrenza e con facilità sistemi di calcolo anche complessi, personalizzati per le proprie esigenze e in grado di velocizzare drasticamente i tempi di esecuzione. Il sistema attualmente mette a disposizione 240 core fisici (corrispondenti a circa 1900 Virtual CPU) e circa 70 TB di storage, che può essere acceduto dalle Virtual Machine istanziate nella Cloud. I servizi di CloudVeneto.it sono stati configurati in alta affidabilità , per assicurarne la massima disponibilità, anche in presenza di guasti o malfunzionamenti di singoli componenti. I servizi inoltre sono stati resi sicuri attraverso l’adozione della tecnologia SSL. E` possibile accedere alle funzionalità di CloudVeneto.it attraverso command line tool, o attraverso una dashboard web-based, autenticandosi attraverso il sistema di Single Sign-On (SSO) dell’Università di Padova o l’Identity Provider dell’INFN (INFN-AAI). Dopo alcuni mesi di utilizzo da parte di alcuni utenti dei dipartimenti coinvolti, l’infrastruttura è ormai entrata in funzionamento a regime e gli utenti possono beneficiare, oltre che della documentazione messa a disposizione, anche del supporto dei sistemisti dell’Università e dell’INFN

The CloudVeneto initiative: 10 years of operations to support interdisciplinary open science

CloudVeneto is a private cloud targeted to scientific communities based on OpenStack software. It was designed in 2013 and put in operation one year later, to support INFN projects, mainly HEP ones. Its resources are physically distributed among two sites: the Physics Department of University of Padova-INFN Padova Unit and the INFN Legnaro National Laboratories. During these 10 years CloudVeneto evolved to integrate also resources funded by ten Departments of the University of Padova, and to support several scientific disciplines of different domains. The use cases the communities have to face up often show a common pattern. This was an opportunity for us to develop and improve the services on our infrastructure to provide common solutions to different use cases. It happened for example with the Container as a Service (CaaS) that makes the management of Kubernetes clusters easier from a user point of view. Moreover, CloudVeneto joined the INFN national cloud infrastructure (INFN Cloud), making available some resources to this federated infrastructure. CloudVeneto is also involved in an R&D project to realize a distributed analysis facility for the CMS experiment based on the HTCondor batch system. In this paper we describe some use-cases of different projects pointing out the common patterns and the new implementations and configurations done in the infrastructure

Observation of the B_c Meson in p-bar p Collisions at sqrt{s} = 1.8 TeV

We have observed bottom-charm mesons B_c via the decay mode Bc -> J/psi lepton neutrino in 1.8 TeV p-bar p collisions using the CDF detector at the Fermilab Tevatron. A fit of background and signal contributions to the J/psi + lepton mass distribution yielded 20.4 +6.2 -5.5 events from B_c mesons. A fit to the same distribution with background alone was rejected at the level of 4.8 standard deviations. We measured the B_c mass to be 6.40 +- 0.39 +- 0.13 GeVc^2 and the B_c lifetime to be tau(B_c) = 0.46 +0.18 -0.16 +- 0.03 ps. We measured the production cross section times branching ratio for B_c -> J/psi lepton neutrino relative to that for B+ -> J/psi K to be 0.132 +0.041 -0.037 (stat) +- 0.031 (syst) +0.032 -0.020 (lifetime).Comment: 13 pages, 3 figures. Submitted to Physical Review Letters. Available at http://www-cdf.fnal.gov/physics/pub98/cdf4496_Bc_PRL.p

Merging OpenStack-based private clouds: the case of CloudVeneto.it

The Cloud Area Padovana, deployed in 2014, is a scientific IaaS cloud, spread between two different sites: the INFN Padova Unit and the INFN Legnaro National Labs. It provides about 1100 logical cores and 50 TB of storage. The entire computing facility, owned by INFN, satisfies the computational and storage demands of more than 100 users belonging to about 30 research projects, mainly related to HEP and nuclear physics. The Padova data centre also has hosted and operated since 2015 an independent IaaS cloud managing network, storage and computing resources owned by 10 departments of the University of Padova, supporting a broader range of scientific and engineering disciplines. This infrastructure provides about 480 logical cores and 90 TB of storage and supports more than 40 research projects. These two clouds share only a limited set of ICT services and tools (mainly for configuration, monitoring and accounting), whereas their daily operations and maintenance are carried out separately by INFN and University personnel. At the end of 2017 we planned to merge the two infrastructures in order to optimise the use of resources (both human and ICT) and to avoid useless duplication of services. We discuss here how we plan to implement this integration, resulting in a single cloud infrastructure named CloudVeneto.it

Merging OpenStack-based private clouds: the case of CloudVeneto.it

The Cloud Area Padovana, deployed in 2014, is a scientific IaaS cloud, spread between two different sites: the INFN Padova Unit and the INFN Legnaro National Labs. It provides about 1100 logical cores and 50 TB of storage. The entire computing facility, owned by INFN, satisfies the computational and storage demands of more than 100 users belonging to about 30 research projects, mainly related to HEP and nuclear physics. The Padova data centre also has hosted and operated since 2015 an independent IaaS cloud managing network, storage and computing resources owned by 10 departments of the University of Padova, supporting a broader range of scientific and engineering disciplines. This infrastructure provides about 480 logical cores and 90 TB of storage and supports more than 40 research projects. These two clouds share only a limited set of ICT services and tools (mainly for configuration, monitoring and accounting), whereas their daily operations and maintenance are carried out separately by INFN and University personnel. At the end of 2017 we planned to merge the two infrastructures in order to optimise the use of resources (both human and ICT) and to avoid useless duplication of services. We discuss here how we plan to implement this integration, resulting in a single cloud infrastructure named CloudVeneto.it

Evolution of the CloudVeneto.it private cloud to support research and innovation

CloudVeneto.it was initially funded and deployed by INFN in 2014 for serving the computational and storage demands of INFN research projects mainly related to HEP and Nuclear Physics. It is an OpenStack-based scientific cloud with resources spread across two different sites connected with a high speed optical link: INFN Padova Unit and the INFN Legnaro National Laboratories. The infrastructure has grown throughout the years with additional funds from ten University of Padova departments, and nowadays supports a broader range of scientific and engineering disciplines. Its hardware resources provide around 2500 computational cores and 360 TB of storage to about 250 users working for more than 70 projects. In the last months we enhanced the cloud platform in two ways: 1) by integrating a number of heterogeneous GPU cards to address the special needs of user communities whose computations involve machine learning training; 2) by enabling the users to simply deploy on-demand Kubernetes clusters for Big Data Analytics applications taking advantage of the operator framework. In particular, the Kubernetes operators for Apache Kafka and Spark platforms were integrated to address real-time data ingestion and streaming processing on the cloud. This article describes the technical details of these two solutions and their integration with the cloud infrastructure

Accounting in the CloudVeneto private cloud

CloudVeneto is a private cloud implemented as the result of merging two existing cloud infrastructures: the INFN Cloud Area Padovana, and a private cloud owned by 10 departments of University of Padova. This infrastructure is a full production facility, in continuous growth, both in terms of users, and in terms of computing and storage resources. Even if the usage of CloudVeneto is not regulated by a strict pay-per-use model, the availability of accounting information for such infrastructure is a requirement, to detect if the resources allocated to the user communities are effciently used, and to perform an effective capacity planning. We present in this paper how the accounting system used in CloudVeneto evolved over time, focusing on the accounting framework being used now, implemented by integrating existing components

Evolution of the CloudVeneto.it private cloud to support research and innovation

CloudVeneto.it was initially funded and deployed by INFN in 2014 for serving the computational and storage demands of INFN research projects mainly related to HEP and Nuclear Physics. It is an OpenStack-based scientific cloud with resources spread across two different sites connected with a high speed optical link: INFN Padova Unit and the INFN Legnaro National Laboratories. The infrastructure has grown throughout the years with additional funds from ten University of Padova departments, and nowadays supports a broader range of scientific and engineering disciplines. Its hardware resources provide around 2500 computational cores and 360 TB of storage to about 250 users working for more than 70 projects. In the last months we enhanced the cloud platform in two ways: 1) by integrating a number of heterogeneous GPU cards to address the special needs of user communities whose computations involve machine learning training; 2) by enabling the users to simply deploy on-demand Kubernetes clusters for Big Data Analytics applications taking advantage of the operator framework. In particular, the Kubernetes operators for Apache Kafka and Spark platforms were integrated to address real-time data ingestion and streaming processing on the cloud. This article describes the technical details of these two solutions and their integration with the cloud infrastructure

Measurement of the B-d(0)-(B)over-bar(d)(0) flavor oscillation frequency and study of same side flavor tagging of B mesons in p(p)over-bar collisions

B-d(0)-(B) over bar(d)(0) oscillations are observed in ''self-tagged'' samples of partially reconstructed B mesons decaying into a lepton and a charmed meson collected in p (p) over bar collisions at root s = 1.8 TeV. A flavor tagging technique is employed which relies upon the correlation between the flavor of B mesons and the charge of a nearby particle. We measure the flavor oscillation frequency to be Delta m(d) = 0.471 (+0.078)(-0.068) +/- 0.034 ps(-1). The tagging method is also demonstrated in exclusive samples of B-u(+) --> J/psi K+ and B-d(0)-->J/psi K-*0(892), where similar flavor-charge correlations are observed. The tagging characteristics of the various samples are compared with each other, and with Monte Carlo simulations. [S0556-2821(99)05901-9]