A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Building A High Performance Parallel File System Using Grid Datafarm and ROOT I/O

Sheer amount of petabyte scale data foreseen in the LHC experiments require a careful consideration of the persistency design and the system design in the world-wide distributed computing. Event parallelism of the HENP data analysis enables us to take maximum advantage of the high performance cluster computing and networking when we keep the parallelism both in the data processing phase, in the data management phase, and in the data transfer phase. A modular architecture of FADS/ Goofy, a versatile detector simulation framework for Geant4, enables an easy choice of plug-in facilities for persistency technologies such as Objectivity/DB and ROOT I/O. The framework is designed to work naturally with the parallel file system of Grid Datafarm (Gfarm). FADS/Goofy is proven to generate 10^6 Geant4-simulated Atlas Mockup events using a 512 CPU PC cluster. The data in ROOT I/O files is replicated using Gfarm file system. The histogram information is collected from the distributed ROOT files. During the data replication it has been demonstrated to achieve more than 2.3 Gbps data transfer rate between the PC clusters over seven participating PC clusters in the United States and in Japan.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, PDF. PSN TUDT01

Deployment Distribuito di codice e dati su Grid mediante Tecniche di Compressione e di Caching

Studio, progettazione e realizzazione di un sistema scalabile di deployment per Grid. Il prototipo realizza il multicast di grandi quantita' di dati tramite decomposizione a blocchi con fingerprinting e replicazione distribuita. Utilizza tecniche di compressione e caching per l’ottimizzazione della banda di rete, dei tempi di accesso ai dati e per riutilizzare i dati frutto di precedenti deployment. Il sistema e' ottimizzato per l’invio di insiemi di file a insiemi di nodi, tutti eventualmente disgiunti. La libreria progettata e realizzata e' in grado di mantenere pressoche' costante il tempo di deployment all’aumentare dei nodi destinatari e riesce a mantenere un’efficienza relativa che arriva fino al 100% all’aumentare della quantita' di dati da inviar

New approaches to data access in large-scale distributed system

Mención Internacional en el título de doctorA great number of scientific projects need supercomputing resources, such as, for example, those carried out in physics, astrophysics, chemistry, pharmacology, etc. Most of them generate, as well, a great amount of data; for example, a some minutes long experiment in a particle accelerator generates several terabytes of data. In the last years, high-performance computing environments have evolved towards large-scale distributed systems such as Grids, Clouds, and Volunteer Computing environments. Managing a great volume of data in these environments means an added huge problem since the data have to travel from one site to another through the internet. In this work a novel generic I/O architecture for large-scale distributed systems used for high-performance and high-throughput computing will be proposed. This solution is based on applying parallel I/O techniques to remote data access. Novel replication and data search schemes will also be proposed; schemes that, combined with the above techniques, will allow to improve the performance of those applications that execute in these environments. In addition, it will be proposed to develop simulation tools that allow to test these and other ideas without needing to use real platforms due to their technical and logistic limitations. An initial prototype of this solution has been evaluated and the results show a noteworthy improvement regarding to data access compared to existing solutions.Un gran número de proyectos científicos necesitan recursos de supercomputación como, por ejemplo, los llevados a cabo en física, astrofísica, química, farmacología, etc. Muchos de ellos generan, además, una gran cantidad de datos; por ejemplo, un experimento de unos minutos de duración en un acelerador de partículas genera varios terabytes de datos. Los entornos de computación de altas prestaciones han evolucionado en los últimos años hacia sistemas distribuidos a gran escala tales como Grids, Clouds y entornos de computación voluntaria. En estos entornos gestionar un gran volumen de datos supone un problema añadido de importantes dimensiones ya que los datos tienen que viajar de un sitio a otro a través de internet. En este trabajo se propondrá una nueva arquitectura de E/S genérica para sistemas distribuidos a gran escala usados para cómputo de altas prestaciones y de alta productividad. Esta solución se basa en la aplicación de técnicas de E/S paralela al acceso remoto a los datos. Así mismo, se estudiarán y propondrán nuevos esquemas de replicación y búsqueda de datos que, en combinación con las técnicas anteriores, permitan mejorar las prestaciones de aquellas aplicaciones que ejecuten en este tipo de entornos. También se propone desarrollar herramientas de simulación que permitan probar estas y otras ideas sin necesidad de recurrir a una plataforma real debido a las limitaciones técnicas y logísticas que ello supone. Se ha evaluado un prototipo inicial de esta solución y los resultados muestran una mejora significativa en el acceso a los datos sobre las soluciones existentes.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: David Expósito Singh.- Secretario: María de los Santos Pérez Hernández.- Vocal: Juan Manuel Tirado Mart

LHCb Data Management on the Computing Grid

The LHCb detector is one of the four experiments being built to harness the proton-proton collisions provided by the Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN). The data rate expected, when the LHC experiments are fully operational, eclipses that of any previous scientific experiments and has motivated the adoption of a grid computing paradigm to store and process the data. Managing PetaBytes of data in a distributed environment provides a rich set of challenges related to scalability, reliability and performance. This thesis will present the data management requirements for executing the workload of the LHCb collab- oration. We present the systems designed that support all aspects of the grid data management for LHCb, from data transfer, to data integrity, and efficient data access. The distributed computing environment is inherently unstable and much focus has been made on providing systems that are ro- bust and resilient to observed failures

Worldwide Fast File Replication on Grid Datafarm

The Grid Datafarm architecture is designed for global petascale data-intensive computing. It provides a global parallel filesystem with online petascale storage, scalable I/O bandwidth, and scalable parallel processing, and it can exploit local I/O in a grid of clusters with tens of thousands of nodes. One of features is that it manages file replicas in filesystem metadata for fault tolerance and load balancing. This paper discusses and evaluates several techniques to support long-distance fast file replication. The Grid Datafarm manages a ranked group of files as a Gfarm file, each file, called a Gfarm file fragment, being stored on a filesystem node, or replicated on several filesystem nodes. Each Gfarm file fragment is replicated independently and in parallel using rate-controlled HighSpeed TCP with network striping. On a US-Japan testbed with 10,000 km distance, we achieve 419 Mbps using 2 nodes on each side, and 741 Mbps using 4 nodes out of 893 Mbps with two transpacific networks. 1