The architecture of discovery net : towards grid-based discovery services

Imperial Users onl

A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications

Open Science is a paradigm in which scientific data, procedures, tools and results are shared transparently and reused by society. The European Open Science Cloud (EOSC) initiative is an effort in Europe to provide an open, trusted, virtual and federated computing environment to execute scientific applications and store, share and reuse research data across borders and scientific disciplines. Additionally, scientific services are becoming increasingly data-intensive, not only in terms of computationally intensive tasks but also in terms of storage resources. To meet those resource demands, computing paradigms such as High-Performance Computing (HPC) and Cloud Computing are applied to e-science applications. However, adapting applications and services to these paradigms is a challenging task, commonly requiring a deep knowledge of the underlying technologies, which often constitutes a general barrier to its uptake by scientists. In this context, EOSC-Synergy, a collaborative project involving more than 20 institutions from eight European countries pooling their knowledge and experience to enhance EOSC’s capabilities and capacities, aims to bring EOSC closer to the scientific communities. This article provides a summary analysis of the adaptations made in the ten thematic services of EOSC-Synergy to embrace this paradigm. These services are grouped into four categories: Earth Observation, Environment, Biomedicine, and Astrophysics. The analysis will lead to the identification of commonalities, best practices and common requirements, regardless of the thematic area of the service. Experience gained from the thematic services can be transferred to new services for the adoption of the EOSC ecosystem framework. The article made several recommendations for the integration of thematic services in the EOSC ecosystem regarding Authentication and Authorization (federated regional or thematic solutions based on EduGAIN mainly), FAIR data and metadata preservation solutions (both at cataloguing and data preservation—such as EUDAT’s B2SHARE), cloud platform-agnostic resource management services (such as Infrastructure Manager) and workload management solutions.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857647, EOSC-Synergy, European Open Science Cloud - Expanding Capacities by building Capabilities. Moreover, this work is partially funded by grant No 2015/24461-2, São Paulo Research Foundation (FAPESP). Francisco Brasileiro is a CNPq/Brazil researcher (grant 308027/2020-5).Peer Reviewed"Article signat per 20 autors/es: Amanda Calatrava, Hernán Asorey, Jan Astalos, Alberto Azevedo, Francesco Benincasa, Ignacio Blanquer, Martin Bobak, Francisco Brasileiro, Laia Codó, Laura del Cano, Borja Esteban, Meritxell Ferret, Josef Handl, Tobias Kerzenmacher, Valentin Kozlov, Aleš Křenek, Ricardo Martins, Manuel Pavesio, Antonio Juan Rubio-Montero, Juan Sánchez-Ferrero "Postprint (published version

Using an Actor Framework for Scientific Computing: Opportunities and Challenges

We examine the challenges and advantages of using an actor framework for programming and execution of scientific workflows. The following specific topics are studied: implementing workflow semantics and typical workflow patterns in the actor model, parallel and distributed execution of workflow activities using actors, leveraging event sourcing as a novel approach for workflow state persistence and recovery, and applying supervision as a fault tolerance model for workflows. In order to practically validate our research, we have created Scaflow, an Akka-based programming library and workflow execution engine. We study an example workflow implemented in Scaflow, and present experimental measurements of workflow persistence overhead

A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications

Open Science is a paradigm in which scientific data, procedures, tools and results are shared transparently and reused by society as a whole. The initiative known as the European Open Science Cloud (EOSC) is an effort in Europe to provide an open, trusted, virtual and federated computing environment to execute scientific applications, and to store, share and re-use research data across borders and scientific disciplines. Additionally, scientific services are becoming increasingly data-intensive, not only in terms of computationally intensive tasks but also in terms of storage resources. Computing paradigms such as High Performance Computing (HPC) and Cloud Computing are applied to e-science applications to meet these demands. However, adapting applications and services to these paradigms is not a trivial task, commonly requiring a deep knowledge of the underlying technologies, which often constitutes a barrier for its uptake by scientists in general. In this context, EOSC-SYNERGY, a collaborative project involving more than 20 institutions from eight European countries pooling their knowledge and experience to enhance EOSC\u27s capabilities and capacities, aims to bring EOSC closer to the scientific communities. This article provides a summary analysis of the adaptations made in the ten thematic services of EOSC-SYNERGY to embrace this paradigm. These services are grouped into four categories: Earth Observation, Environment, Biomedicine, and Astrophysics. The analysis will lead to the identification of commonalities, best practices and common requirements, regardless of the thematic area of the service. Experience gained from the thematic services could be transferred to new services for the adoption of the EOSC ecosystem framework

Locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data, A

2017 Spring.Includes bibliographical references.Discerning knowledge from voluminous data involves a series of data manipulation steps. Scientists typically compose and execute workflows for these steps using scientific workflow management systems (SWfMSs). SWfMSs have been developed for several research communities including but not limited to bioinformatics, biology, astronomy, computational science, and physics. Parallel execution of workflows has been widely employed in SWfMSs by exploiting the storage and computing resources of grid and cloud services. However, none of these systems have been tailored for the needs of spatiotemporal analytics on real-time sensor data with high arrival rates. This thesis demonstrates the development and evaluation of a target-oriented workflow model that enables a user to specify dependencies among the workflow components, including data availability. The underlying spatiotemporal data dispersion and indexing scheme provides fast data search and retrieval to plan and execute computations comprising the workflow. This work includes a scheduling algorithm that targets minimizing data movement across machines while ensuring fair and efficient resource allocation among multiple users. The study includes empirical evaluations performed on the Google cloud

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures