Grid computing and molecular simulations: the vision of the eMinerals Project

This paper discusses a number of aspects of using grid computing methods in support of molecular simulations, with examples drawn from the eMinerals project. A number of components for a useful grid infrastructure are discussed, including the integration of compute and data grids, automatic metadata capture from simulation studies, interoperability of data between simulation codes, management of data and data accessibility, management of jobs and workflow, and tools to support collaboration. Use of a grid infrastructure also brings certain challenges, which are discussed. These include making use of boundless computing resources, the necessary changes, and the need to be able to manage experimentation

DIRAC framework evaluation for the Fermi\boldsymbol{Fermi}-LAT and CTA experiments

DIRAC (Distributed Infrastructure with Remote Agent Control) is a general framework for the management of tasks over distributed heterogeneous computing environments. It has been originally developed to support the production activities of the LHCb (Large Hadron Collider Beauty) experiment and today is extensively used by several particle physics and biology communities. Current ($Fermi$ Large Area Telescope -- LAT) and planned (Cherenkov Telescope Array -- CTA) new generation astrophysical/cosmological experiments, with very large processing and storage needs, are currently investigating the usability of DIRAC in this context. Each of these use cases has some peculiarities: $Fermi$-LAT will interface DIRAC to its own workflow system to allow the access to the grid resources, while CTA is using DIRAC as workflow management system for Monte Carlo production and analysis on the grid. We describe the prototype effort that we lead toward deploying a DIRAC solution for some aspects of $Fermi$-LAT and CTA needs.Comment: proceedings to CHEP 2013 conference : http://www.chep2013.org

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu)

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Grid-enabled Workflows for Industrial Product Design

This paper presents a generic approach for developing and using Grid-based workflow technology for enabling cross-organizational engineering applications. Using industrial product design examples from the automotive and aerospace industries we highlight the main requirements and challenges addressed by our approach and describe how it can be used for enabling interoperability between heterogeneous workflow engines

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