Gestión Autonómica de Energía y Fallos en Clústeres de Computadores

El consumo de energía de grandes clústeres de ordenadores afecta a los costes de explotación de centros de datos, en particular incrementando el coste total de posesión (TCO). Por lo tanto, la utilización de algoritmos de minimización de la energía es esencial para una explotación rentable de los centros de datos. El gestor energético presentado en este trabajo se basa en un algoritmo de provisión dinámica operado de forma autonómica, de forma que se reducen las intervenciones humanas y el sistema es capaz de responder de forma autónoma ante errores, además de operar siempre con un consumo energético mínimo. Asimismo, la tolerancia a fallos en los grandes centros de datos es fundamental para optimizar el uso de los recursos del mismo y garantizar la consistencia de los datos y del procesamiento que se está llevando a cabo. En un sistema con provisión dinámica de nodos, donde la topología del clúster es gestionada por un elemento de gestión de carga, es posible aprovechar esta característica para remplazar nodos problemáticos cuando otros estén disponibles. Este trabajo presenta un sistema de tolerancia a fallos que se integra con el algoritmo de optimización energética basado en provisión dinámica de nodos y permite incrementar la tolerancia a fallos garantizando el cumplimiento del SLA en diversas situaciones de carga y composición del clúster

Commissioning the CERN IT Agile Infrastructure with experiment workloads

In order to ease the management of their infrastructure, most of the WLCG sites are adopting cloud based strategies. In the case of CERN, the Tier 0 of the WLCG, is completely restructuring the resource and configuration management of their computing center under the codename Agile Infrastructure. Its goal is to manage 15,000 Virtual Machines by means of an OpenStack middleware in order to unify all the resources in CERN's two datacenters: the one placed in Meyrin and the new on in Wigner, Hungary. During the commissioning of this infrastructure, CERN IT is offering an attractive amount of computing resources to the experiments (800 cores for ATLAS and CMS) through a private cloud interface. ATLAS and CMS have joined forces to exploit them by running stress tests and simulation workloads since November 2012. This work will describe the experience of the first deployments of the current experiment workloads on the CERN private cloud testbed. The paper is organized as follows: the first section will explain the integration of the experiment workload management systems (WMS) with the cloud resources. The second section will revisit the performance and stress testing performed with HammerCloud in order to evaluate and compare the suitability for the experiment workloads. The third section will go deeper into the dynamic provisioning techniques, such as the use of the cloud APIs directly by the WMS. The paper finishes with a review of the conclusions and the challenges ahead

Helix Nebula and CERN: A Symbiotic approach to exploiting commercial clouds

The recent paradigm shift toward cloud computing in IT, and general interest in "Big Data" in particular, have demonstrated that the computing requirements of HEP are no longer globally unique. Indeed, the CERN IT department and LHC experiments have already made significant R&D investments in delivering and exploiting cloud computing resources. While a number of technical evaluations of interesting commercial offerings from global IT enterprises have been performed by various physics labs, further technical, security, sociological, and legal issues need to be address before their large-scale adoption by the research community can be envisaged. Helix Nebula - the Science Cloud is an initiative that explores these questions by joining the forces of three European research institutes (CERN, ESA and EMBL) with leading European commercial IT enterprises. The goals of Helix Nebula are to establish a cloud platform federating multiple commercial cloud providers, along with new business models, which can sustain the cloud marketplace for years to come. This contribution will summarize the participation of CERN in Helix Nebula. We will explain CERN's flagship use-case and the model used to integrate several cloud providers with an LHC experiment's workload management system. During the first proof of concept, this project contributed over 40.000 CPU-days of Monte Carlo production throughput to the ATLAS experiment with marginal manpower required. CERN's experience, together with that of ESA and EMBL, is providing a great insight into the cloud computing industry and highlighted several challenges that are being tackled in order to ease the export of the scientific workloads to the cloud environments

Testing as a Service with HammerCloud

HammerCloud was designed and born under the needs of the grid community to test the resources and automate operations from a user perspective. The recent developments in the IT space propose a shift to the software defined data centres, in which every layer of the infrastructure can be offered as a service. Testing and monitoring is an integral part of the development, validation and operations of big systems, like the grid. This area is not escaping the paradigm shift and we are starting to perceive as natural the Testing as a Service (TaaS) offerings, which allow testing any infrastructure service, such as the Infrastructure as a Service (IaaS) platforms being deployed in many grid sites, both from the functional and stressing perspectives. This work will review the recent developments in HammerCloud and its evolution to a TaaS conception, in particular its deployment on the Agile Infrastructure platform at CERN and the testing of many IaaS providers across Europe in the context of experiment requirements. The first section will review the architectural changes that a service running in the cloud needs, such an orchestration service or new storage requirements in order to provide functional and stress testing. The second section will review the first tests of infrastructure providers on the perspective of the challenges discovered from the architectural point of view. Finally, the third section will evaluate future requirements of scalability and features to increase testing productivity

An original Eurasian haplotype, HLA-DRB1*14:54-DQB1*05:03, influences the susceptibility to idiopathic achalasia

<div><p>Idiopathic achalasia is a relatively infrequent esophageal motor disorder for which major histocompatibility complex (MHC) genes are well-identified risk factors. However, no information about HLA-achalasia susceptibility in Mexicans has previously been reported. We studied a group of 91 patients diagnosed with achalasia and 234 healthy controls with Mexican admixed ancestry. HLA alleles and conserved extended haplotypes were analyzed using high-resolution HLA typing based on Sanger and next-generation sequencing technologies. Admixture estimates were determined using HLA-B and short tandem repeats. Results were analyzed by non-parametric statistical analysis and Bonferroni correction. P-values < 0.05 were considered significant. Patients with achalasia had 56.7% Native American genes, 24.7% European genes, 16.5% African genes and 2.0% Asian genes, which was comparable with the estimates in the controls. Significant increases in the frequencies of alleles DRB1*14:54 and DQB1*05:03 and the extended haplotypes DRB1*14:54-DQB1*05:03 and DRB1*11:01-DQB1*03:01, even after Bonferroni correction (<i>p</i>C<0.05), were found in the achalasia group compared to those in the controls. Concluding, the HLA class II alleles HLA-DRB1*14:54:01 and DQB1*05:03:01 and the extended haplotype are risk factors for achalasia in mixed-ancestry Mexican individuals. These results also suggest that the HLA-DRB1*14:54-DQB1*05:03 haplotype was introduced by admixture with European and/or Asian populations.</p></div

Gene frequencies of HLA-DRB1 in achalasia patients and healthy controls.

<p>Gene frequencies of HLA-DRB1 in achalasia patients and healthy controls.</p