DIRAC Infrastructure for Distributed Analysis

DIRAC is the LHCb Workload and Data Management system for Monte Carlo simulation, data processing and distributed user analysis. Using DIRAC, a variety of resources may be integrated, including individual PC's, local batch systems and the LCG grid. We report here on the progress made in extending DIRAC for distributed user analysis on LCG. In this paper we describe the advances in the workload management paradigm for analysis with computing resource reservation by means of Pilot Agents. This approach allows DIRAC to mask any inefficiencies of the underlying Grid from the user thus increasing the effective performance of the distributed computing system. The modular design of DIRAC at every level lends the system intrinsic flexibility. The possible strategy for the evolution of the system will be discussed. The DIRAC API consolidates new and existing services and provides a transparent and secure way for users to submit jobs to the Grid. Jobs find their input data by interrogating the LCG File Catalogue which the LCG Resource Broker also uses to determine suitable destination sites. While it may be exploited directly by users, it also serves as the interface for the GANGA Grid front-end to perform distributed user analysis for LHCb. DIRAC has been successfully used to demonstrate distributed data analysis on LCG for LHCb. The system performance results are presented and the experience gained is discussed

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

Grid Data Management in Action: Experience in Running and Supporting Data Management Services in the EU DataGrid Project

In the first phase of the EU DataGrid (EDG) project, a Data Management System has been implemented and provided for deployment. The components of the current EDG Testbed are: a prototype of a Replica Manager Service built around the basic services provided by Globus, a centralised Replica Catalogue to store information about physical locations of files, and the Grid Data Mirroring Package (GDMP) that is widely used in various HEP collaborations in Europe and the US for data mirroring. During this year these services have been refined and made more robust so that they are fit to be used in a pre-production environment. Application users have been using this first release of the Data Management Services for more than a year. In the paper we present the components and their interaction, our implementation and experience as well as the feedback received from our user communities. We have resolved not only issues regarding integration with other EDG service components but also many of the interoperability issues with components of our partner projects in Europe and the U.S. The paper concludes with the basic lessons learned during this operation. These conclusions provide the motivation for the architecture of the next generation of Data Management Services that will be deployed in EDG during 2003.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 9 pages, LaTeX, PSN: TUAT007 all figures are in the directory "figures

Secure, performance-oriented data management for nanoCMOS electronics

The EPSRC pilot project Meeting the Design Challenges of nanoCMOS Electronics (nanoCMOS) is focused upon delivering a production level e-Infrastructure to meet the challenges facing the semiconductor industry in dealing with the next generation of ‘atomic-scale’ transistor devices. This scale means that previous assumptions on the uniformity of transistor devices in electronics circuit and systems design are no longer valid, and the industry as a whole must deal with variability throughout the design process. Infrastructures to tackle this problem must provide seamless access to very large HPC resources for computationally expensive simulation of statistic ensembles of microscopically varying physical devices, and manage the many hundreds of thousands of files and meta-data associated with these simulations. A key challenge in undertaking this is in protecting the intellectual property associated with the data, simulations and design process as a whole. In this paper we present the nanoCMOS infrastructure and outline an evaluation undertaken on the Storage Resource Broker (SRB) and the Andrew File System (AFS) considering in particular the extent that they meet the performance and security requirements of the nanoCMOS domain. We also describe how metadata management is supported and linked to simulations and results in a scalable and secure manner

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

Cloud Storage Performance and Security Analysis with Hadoop and GridFTP

Even though cloud server has been around for a few years, most of the web hosts today have not converted to cloud yet. If the purpose of the cloud server is distributing and storing files on the internet, FTP servers were much earlier than the cloud. FTP server is sufficient to distribute content on the internet. Therefore, is it worth to shift from FTP server to cloud server? The cloud storage provider declares high durability and availability for their users, and the ability to scale up for more storage space easily could save users tons of money. However, does it provide higher performance and better security features? Hadoop is a very popular platform for cloud computing. It is free software under Apache License. It is written in Java and supports large data processing in a distributed environment. Characteristics of Hadoop include partitioning of data, computing across thousands of hosts, and executing application computations in parallel. Hadoop Distributed File System allows rapid data transfer up to thousands of terabytes, and is capable of operating even in the case of node failure. GridFTP supports high-speed data transfer for wide-area networks. It is based on the FTP and features multiple data channels for parallel transfers. This report describes the technology behind HDFS and enhancement to the Hadoop security features with Kerberos. Based on data transfer performance and security features of HDFS and GridFTP server, we can decide if we should replace GridFTP server with HDFS. According to our experiment result, we conclude that GridFTP server provides better throughput than HDFS, and Kerberos has minimal impact to HDFS performance. We proposed a solution which users authenticate with HDFS first, and get the file from HDFS server to the client using GridFTP