Performance, reliability and scalability in data-access are key issues in the context of the computing Grid and High Energy Physics data processing and analysis applications, in particular considering the large data size and I/O load that a Large Hadron Collider data centre has to support. In this paper we present the technical details and the results of a large scale validation and performance measurement employing different data-access platforms-namely CASTOR, dCache, GPFS and Scalla/Xrootd. The tests have been performed at the CNAF Tier-1, the central computing facility of the Italian National Institute for Nuclear Research (INFN). Our storage back-end was based on Fibre Channel disk-servers organized in a Storage Area Network, being the disk-servers connected to the computing farm via Gigabit LAN. We used 24 disk-servers, 260 TB of raw-disk space and 280 worker nodes as computing clients, able to run concurrently up to about 1100 jobs. The aim of the test was to perform sequential and random read/write accesses to the data, as well as more realistic access patterns, in order to evaluate efficiency, availability, robustness and performance of the various data-access solutions.

A Comparison of Data-Access Platforms for the Computing of Large Hadron Collider Experiments

In recent times a new kind of computing system has emerged: a distributed infrastructure composed of multiple physical sites in different administrative domains. This model introduces significant new challenges: common configuration parameters must be shared, local customization must be supported, and policy domains must be respected. We believe that such features can best be implemented by a system that provides a high-level configuration language (allowing structuring and validation of configuration information) and that is modular (allowing for flexible structuring of the overall infrastructure).

The Quattor configuration management toolkit has been designed to meet these requirements. Quattor uses a declarative model where high-level descriptions are translated into configurations and enacted by autonomous components running on the configured machines. Quattor's Pan language provides features for composing complex configuration schemes in a hierarchical manner, for structuring configuration information along node or service lines, and for validating parameters. Finally, the modular architecture of Quattor allows great flexibility in the placement of configuration servers within a distributed infrastructure.

Quattor is being successfully used to manage distributed grid infrastructures in three countries. The suitability of the Pan language is demonstrated by the fact that a comprehensive distribution of configuration templates has been developed as a community effort.

/pdf/devolved-management-of-distributed-infrastructures-with-2fgelshsgg.pdf

Devolved management of distributed infrastructures with Quattor

The ALICE (A Large Ion Collider Experiment) Data Acquisition (DAQ) system has the unprecedented requirement to ensure a very high volume, sustained data stream between the ALICE Detector and the Permanent Data Storage (PDS) system which is used as main data repository for Event processing and Offline Computing. The key component to accomplish this task is the Transient Data Storage System (TDS), a set of data storage elements with its associated hardware and software components, which supports raw data collection, its conversion into a format suitable for subsequent high-level analysis, the storage of the result using highly parallelized architectures, its access via a cluster file system capable of creating high-speed partitions via its affinity feature, and its transfer to the final destination via dedicated data links. We describe the methods and the components used to validate, test, implement, operate, and monitor the ALICE Online Data Storage system and the way it has been used in the early days of commissioning and operation for the ALICE Detector. We will also introduce the future developments needed from next year, when the ALICE Data Acquisition System will shift its requirements from those associated to the test and commissioning phase to those imposed by long-duration data taking periods alternated by shorter validation and maintenance tasks which will be needed to adequately operate the ALICE Experiment.

http://iopscience.iop.org/article/10.1088/1742-6596/219/5/052002/pdf

The ALICE online data storage system

Performance, quality and reliability issues are becoming increasingly important for the emerging technologies like computing technology (CT). The purpose of this work is comparing and presenting operational performance of the grid nodes particularly in South Asia. The data analyzed here are derived from monitoring data of LCG (Large Hadron Collider Grid). The performance potential is analyzed by extracting data from the operational conditions of the grid sites. A grid computing facility was developed at sister institutes: Pakistan Institute of Nuclear Science and Technology (PINSTECH) and Pakistan Institute of Engineering and Applied Sciences (PIEAS), in collaboration with Large Hadron Collider (LHC) Computing Grid during early years of the present decade. The Grid facility “PAKGRID-LCG2” as one of the grid node in Pakistan was developed employing mainly local means and is capable of supporting local and international research and computational tasks in the domain of LHC Computing Grid. Functional status of the facility is presented in terms of number of jobs performed. The facility provides a forum to local researchers in the field of high energy physics to participate in the LHC experiments and related activities at European particle physics research laboratory (CERN), which is one of the best physics laboratories in the world. It also provides a platform of the emerging computing technology.

Grid computing in Pakistan: A case study of opening to large hadron collider experiments at CERN

Performance, reliability and scalability in data access are key issues in the context of Grid computing and High Energy Physics (HEP) data analysis. We present the technical details and the results of a large scale validation and performance measurement achieved at the INFN Tier1, the central computing facility of the Italian National Institute for Nuclear Research (INFN). The aim of this work is the evaluation of data access activity during analysis tasks within BaBar computing model against two of the most used data handling systems in HEP scenario: GPFS and Scalla/Xrootd.

A comparison of data-access platforms for the computing of BaBar experiment at the Italian Tier1

/pdf/current-status-of-fabric-management-at-cern-hsvlmzdlrb.pdf

Current Status of Fabric Management at CERN

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Current Status of Fabric Management at CERN