Prototyping Virtual Data Technologies in ATLAS Data Challenge 1 Production

For efficiency of the large production tasks distributed worldwide, it is essential to provide shared production management tools comprised of integratable and interoperable services. To enhance the ATLAS DC1 production toolkit, we introduced and tested a Virtual Data services component. For each major data transformation step identified in the ATLAS data processing pipeline (event generation, detector simulation, background pile-up and digitization, etc) the Virtual Data Cookbook (VDC) catalogue encapsulates the specific data transformation knowledge and the validated parameters settings that must be provided before the data transformation invocation. To provide for local-remote transparency during DC1 production, the VDC database server delivered in a controlled way both the validated production parameters and the templated production recipes for thousands of the event generation and detector simulation jobs around the world, simplifying the production management solutions.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 5 pages, 3 figures, pdf. PSN TUCP01

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

A GRID-BASED E-LEARNING MODEL FOR OPEN UNIVERSITIES

E-learning has grown to become a widely accepted method of learning all over the world. As a result, many e-learning platforms which have been developed based on varying technologies were faced with some limitations ranging from storage capability, computing power, to availability or access to the learning support infrastructures. This has brought about the need to develop ways to effectively manage and share the limited resources available in the e-learning platform. Grid computing technology has the capability to enhance the quality of pedagogy on the e-learning platform. In this paper we propose a Grid-based e-learning model for Open Universities. An attribute of such universities is the setting up of multiple remotely located campuses within a country. The grid-based e-learning model presented in this work possesses the attributes of an elegant architectural framework that will facilitate efficient use of available e-learning resources and cost reduction, leading to general improvement of the overall quality of the operations of open universities