Workpackage 8 of the European Datagrid project was formed in January 2001 with representatives from the four LHC experiments, and with experiment independent people from five of the six main EDG partners. In September 2002 WP8 was strengthened by the addition of effort from BaBar and D0. The original mandate of WP8 was, following the definition of short- and long-term requirements, to port experiment software to the EDG middleware and testbed environment. A major additional activity has been testing the basic functionality and performance of this environment. This paper reviews experiences and evaluations in the areas of job submission, data management, mass storage handling, information systems and monitoring. It also comments on the problems of remote debugging, the portability of code, and scaling problems with increasing numbers of jobs, sites and nodes. Reference is made to the pioneeering work of Atlas and CMS in integrating the use of the EDG Testbed into their data challenges. A forward look is made to essential software developments within EDG and to the necessary cooperation between EDG and LCG for the LCG prototype due in mid 2003.

/pdf/hep-applications-evaluation-of-the-edg-testbed-and-4p8ehhgehc.pdf

HEP Applications Evaluation of the EDG Testbed and Middleware

Today, large-scale scientific applications are both data driven and distributed. To support the scale and inherent distribution of these applications, significant heterogeneous and geographically distributed resources are required over long periods of time to ensure adequate performance. Furthermore, the behavior of these applications depends on a large number of factors related to the application, the system software, the underlying hardware, and other running applications, as well as potential interactions among these factors. 
Most Grid application users are primarily concerned with obtaining the result of the application as fast as possible, without worrying about the details involved in monitoring and understanding factors affecting application performance. In this work, we aim to provide the application users with a simple and intuitive performance evaluation mechanism during the execution time of their long-running Grid applications or workflows. Our performance evaluation mechanism provides a qualitative and periodic assessment of the application's behavior by informing the user whether the application's performance is expected or unexpected. Furthermore, it can help improve overall application performance by informing and guiding fault-tolerance services when the application exhibits persistent unexpected performance behaviors. 
This thesis addresses the hypotheses that in order to qualitatively assess application behavioral states in long-running scientific Grid applications: (1) it is necessary to extract temporal information in performance time series data, and that (2) it is sufficient to extract variance and pattern as specific examples of temporal information. Evidence supporting these hypotheses can lead to the ability to qualitatively assess the overall behavior of the application and, if needed, to offer a most likely diagnostic of the underlying problem. 
To test the stated hypotheses, we develop and evaluate a general qualitative performance analysis framework that incorporates (a) techniques from time series analysis and machine learning to extract and learn from data, structural and temporal features associated with application performance in order to reach a qualitative interpretation of the application's behavior, and (b) mechanisms and policies to reason over time and across the distributed resource space about the behavior of the application. 
Experiments with two scientific applications from meteorology and astronomy comparing signatures generated from instantaneous values of performance data versus those generated from temporal characteristics support the former hypothesis that temporal information is necessary to extract from performance time series data to be able to accurately interpret the behavior of these applications. Furthermore, temporal signatures incorporating variance and pattern information generated for these applications reveal signatures that have distinct characteristics during well-performing versus poor-performing executions. This leads to the framework's accurate classification of instances of similar behaviors, which represents supporting evidence for the latter hypothesis. The proposed framework's ability to generate a qualitative assessment of performance behavior for scientific applications using temporal information present in performance time series data represents a step towards simplifying and improving the quality of service for Grid applications.

Qualitative Performance Analysis for Large-Scale Scientific Workflows

An overview is presented of the characteristics of HEP computing and its mapping to the Grid paradigm. This is followed by a synopsis of the main experiences and lessons learned by HEP experiments in their use of DataGrid middleware using both the EDG application testbed and the LCG production service. Particular reference is made to experiment ‘data challenges’, and a forward look is given to necessary developments in the framework of the EGEE project.

HEP Applications and Their Experience with the Use of DataGrid Middleware

Today's de facto standard for assigning trust, involving X.509 proxy credentials and virtual organizations, is analyzed and found to present a number of problems. A model is proposed which is simpler and has less inherent security risks while still providing the functionality needed by current grid applications.

The Case for a Simpler Security Model in Grid Computing

We present an evaluation of the European Data Grid software in the framework of the BaBar experiment Two kinds of applications have been considered: first, a typical data analysis on real data producing physics n-tuples, and second, a distributed Monte-Carlo production on a computational grid Both applications will be crucial in a near future in order to make an optimal use of the distributed computing resources available throughout the collaboration

/pdf/use-of-the-european-data-grid-software-in-the-framework-of-4ff3hdgiu9.pdf

Use of the European Data Grid software in the framework of the BaBar distributed computing model

We present an evaluation of the European Data Grid software in the framework of the BaBar experiment. Two kinds of applications have been considered: first, a typical data analysis on real data producing physics n-tuples, and second, a distributed Monte-Carlo production on a computational grid. Both applications will be crucial in a near future in order to make an optimal use of the distributed computing resources available throughout the collaboration.

This paper discusses the use of e-science grid in providing computational resources for modern international high energy physics (HEP) experiments. We investigate the suitability of the current generation of grid software to provide the necessary resources to perform large-scale simulation of the experiment and analysis of data in the context of multinational collaboration.

Grid Applications for High Energy Physics Experiments

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Use of the European Data Grid software in the framework of the BaBar distributed computing model

Use of the European Data Grid software in the framework of the BaBar distributed computing model

Grid Applications for High Energy Physics Experiments