Morris Riedel

Unicore — From project results to production grids

The UNICORE Grid-technology provides a seamless, secure and intuitive access to distributed Grid resources. In this paper we present the recent evolution from project results to production Grids. At the beginning UNICORE was developed as a prototype software in two projects funded by the German research ministry (BMBF). Over the following years, in various European-funded projects, UNICORE evolved to a full-grown and well-tested Grid middleware system, which today is used in daily production at many supercomputing centers worldwide. Beyond this production usage, the UNICORE technology serves as a solid basis in many European and International research projects, which use existing UNICORE components to implement advanced features, high level services, and support for applications from a growing range of domains. In order to foster these ongoing developments, UNICORE is available as open source under BSD licence at Source Forge, where new releases are published on a regular basis. This paper is a review of the UNICORE achievements so far and gives a glimpse on the UNICORE roadmap.

A data infrastructure reference model with applications: Towards realization of a ScienceTube vision with a data replication service

AbstractThe wide variety of scientific user communities work with data since many years and thus have already a wide variety of data infrastructures in production today. The aim of this paper is thus not to create one new general data architecture that would fail to be adopted by each and any individual user community. Instead this contribution aims to design a reference model with abstract entities that is able to federate existing concrete infrastructures under one umbrella. A reference model is an abstract framework for understanding significant entities and relationships between them and thus helps to understand existing data infrastructures when comparing them in terms of functionality, services, and boundary conditions. A derived architecture from such a reference model then can be used to create a federated architecture that builds on the existing infrastructures that could align to a major common vision. This common vision is named as ’ScienceTube’ as part of this contribution that determines the high-level goal that the reference model aims to support. This paper will describe how a well-focused use case around data replication and its related activities in the EUDAT project aim to provide a first step towards this vision. Concrete stakeholder requirements arising from scientific end users such as those of the European Strategy Forum on Research Infrastructure (ESFRI) projects underpin this contribution with clear evidence that the EUDAT activities are bottom-up thus providing real solutions towards the so often only described ’high-level big data challenges’. The followed federated approach taking advantage of community and data centers (with large computational resources) further describes how data replication services enable data-intensive computing of terabytes or even petabytes of data emerging from ESFRI projects.

DEISA--Distributed European Infrastructure for Supercomputing Applications

The paper presents an overview of the current research and achievements of the DEISA project, with a focus on the general concept of the infrastructure, the operational model, application projects and science communities, the DEISA Extreme Computing Initiative, user and application support, operations and technology, services, collaborations and interoperability, and the use of standards and policies. The paper concludes with a discussion about the long-term sustainability of the DEISA infrastructure.

GridBeans: Support e-Science and Grid Applications

Large-scale scientific research often relies on the collaborative use of Grid and e-Science infrastructures that provide computational or storage related resources. One of the ideas of these modern infrastructures is to facilitate the routine interaction of scientists and their workflows with advanced problem solving tools and computational resources. While many production Grid projects and e-Science infrastructures have begun to offer services for the usage of resources to end-users during the past several years, the corresponding emerging standards defined by GGF and OASIS still appear to be in flux. In this paper, we present the GridBean technology that bridges the gap between the constantly changing basic Grid or e-Science infrastructures and the need of stable application development environments for the Grid users.

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land-cover classes using the data in order to understand, e.g., impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term big data that stand for challenges shared with many other scientific disciplines. On one hand, the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g., dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of big data toward smaller smart data contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to smart data analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.

Research advances by using interoperable e-science infrastructures

Computational simulations and thus scientific computing is the third pillar alongside theory and experiment in todays science. The term e-science evolved as a new research field that focuses on collaboration in key areas of science using next generation computing infrastructures (i.e. co-called e-science infrastructures) to extend the potential of scientific computing. During the past years, significant international and broader interdisciplinary research is increasingly carried out by global collaborations that often share a single e-science infrastructure. More recently, increasing complexity of e-science applications that embrace multiple physical models (i.e. multi-physics) and consider a larger range of scales (i.e. multi-scale) is creating a steadily growing demand for world-wide interoperable infrastructures that allow for new innovative types of e-science by jointly using different kinds of e-science infrastructures. But interoperable infrastructures are still not seamlessly provided today and we argue that this is due to the absence of a realistically implementable infrastructure reference model. Therefore, the fundamental goal of this paper is to provide insights into our proposed infrastructure reference model that represents a trimmed down version of ogsa in terms of functionality and complexity, while on the other hand being more specific and thus easier to implement. The proposed reference model is underpinned with experiences gained from e-science applications that achieve research advances by using interoperable e-science infrastructures.

Open Standards-Based Interoperability of Job Submission and Management Interfaces across the Grid Middleware Platforms gLite and UNICORE

In a distributed grid environment with ambitious service demands the job submission and management interfaces provide functionality of major importance. Emerging e-science and grid infrastructures such as EGEE and DEISA rely on highly available services that are capable of managing scientific jobs. It is the adoption of emerging open standard interfaces which allows the distribution of grid resources in such a way that their actual service implementation or grid technologies are not isolated from each other, especially when these resources are deployed in different e-science infrastructures that consist of different types of computational resources. This paper motivates the interoperability of these infrastructures and discusses solutions. We describe the adoption of various open standards that recently emerged from the open grid forum (OGF) in the field of job submission and management by well-known grid technologies, respectively gLite and UNICORE. This has a fundamental impact on the interoperability between these technologies and thus within the next generation e-science infrastructures that rely on these technologies.

Towards next generations of software for distributed infrastructures: The European Middleware Initiative

The last two decades have seen an exceptional increase of the available networking, computing and storage resources. Scientific research communities have exploited these enhanced capabilities developing large scale collaborations, supported by distributed infrastructures. In order to enable usage of such infrastructures, several middleware solutions have been created. However such solutions, having been developed separately, have been resulting often in incompatible middleware and infrastructures. The European Middleware Initiative (EMI) is a collaboration, started in 2010, among the major European middleware providers (ARC, dCache, gLite, UNICORE), aiming to consolidate and evolve the existing middleware stacks, facilitating their interoperability and their deployment on large distributed infrastructures, establishing at the same time a sustainable model for the future maintenance and evolution of the middleware components. This paper presents the strategy followed for the achievements of these goals : after an analysis of the situation before EMI, it is given an overview of the development strategy, followed by the most notable technical results, grouped according to the four development areas (Compute, Data, Infrastructure, Security). The rigorous process ensuring the quality of provided software is then illustrated, followed by a description the release process, and of the relations with the user communities. The last section provides an outlook to the future, focusing on the undergoing actions looking toward the sustainability of activities.

Classification of Different Approaches for e-Science Applications in Next Generation Computing Infrastructures

Simulation and thus scientific computing is the third pillar alongside theory and experiment in todays science and engineering. The term e-science evolved as a new research field that focuses on collaboration in key areas of science using next generation infrastructures to extend the powers of scientific computing. This paper contributes to the field of e-science as a study of how scientists actually work within currently existing Grid and e-science infrastructures. Alongside numerous different scientific applications, we identified several common approaches with similar characteristics in different domains. These approaches are described together with a classification on how to perform e-science in next generation infrastructures. The paper is thus a survey paper which provides an overview of the e-science research domain.

Web Services Interfaces and Open Standards Integration into the European UNICORE 6 Grid Middleware

The UNICORE grid system provides a seamless, secure and intuitive access to distributed grid resources. In recent years, UNICORE 5 is used as a well-tested grid middleware system in production grids (e.g. DEISA, D-Grid) and at many supercomputer centers world-wide. Beyond this production usage, UNICORE serves as a solid basis in many European and International research projects and business scenarios from T-Systems, Philips Research, Intel, Fujitsu and others. To foster ongoing developments in multiple projects, UNICORE is open source under BSD license at SourceForge. More recently, the new Web services-based UNICORE 6 has become available that is based on open standards such as the Web services addressing (WS-A) and the Web services resource framework (WS-RF) and thus conforms to the open grid services architecture (OGSA) of the open grid forum (OGF). In this paper we present the evolution from production UNICORE 5 to the open standards-based UNICORE 6 and its various Web services-based interfaces. It describes the interface integration of emerging open standards such as OGSA-BES and OGSA-RUS and thus provides an overview of UNICORE 6.