Service Oriented Architectures for Science Gateways on Grid Systems

Gannon, Dennis; Plale, Beth; Christie, Marcus; Fang, Liang; Huang, Yi; Jensen, Scott A.; Kandaswamy, Gopi; Marru, Suresh; Pallickara, Shrideep; Shirasuna, Satoshi; Simmhan, Yogesh; Slominski, Aleksander; Sun, Yiming

doi:10.1007/11596141_3

Cited by 24 publications

(25 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This grid middleware went through several versions to incorporate many standards and new services, including UNICORE 6 [73]. Major grid deployments include the Large Hadron Collider (LHC) Computing Grid [74], Open Science Grid [75], TeraGrid [76], UK National Grid Service [77], German D-Grid [78], and XSEDE [79]. European grid projects comprised of European Data Grid (EDG) in 2001 [80], Enabling Grids for e-Science in Europe in 2004 [81], Business Experiments in Grid (BEinGRID) in 2006 [82], and European Grid Infrastructure (EGI) in 2010 [83].…”

Section: Grid Computingmentioning

confidence: 99%

High‐performance computing: to boldly go where no human has gone before

Limet

Smari

Spalazzi

2015

Concurrency and Computation

View full text Add to dashboard Cite

International audienceComputing and computational science are well on their way to enter into the exascale era while High Performance Computing (HPC) is now present in many spheres and domains of modern society. New technologies such as cloud computing, big data, and connected objects open many perspectives and possibilities that were unattainable until now. These are high times for computing and for HPC in particular. The High Performance Computing \& Simulation conference aims to bring together researchers working on various aspects of HPC, their design and use, and their applications. As has been the conference tradition, selected extended papers of HPCS 2012 are presented in this special issue. These should be interesting contributions that supplement the current state-of-the-art research in HPC. To relate these works and highlight their value, in this article, we briefly trace back the history of HPC, sketch the state of the present research and development in the field, and project some of the challenges and future trends anticipated

show abstract

Section: Grid Computingmentioning

confidence: 99%

High‐performance computing: to boldly go where no human has gone before

Limet

Smari

Spalazzi

2015

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Although the distinction between traditional web services and Grid services is subtle, Grid services, ideally, should enable virtualization for building and running applications that span organizations and share resources and infrastructure in a seamless way. Gannon et al (2005) and Foster (2002) provide the distinguishing characteristics of a Grid service and specify what is needed for a web service to qualify as a Grid service.…”

Section: Information Technology Trendsmentioning

confidence: 99%

A new generation of cyberinfrastructure and data services for earth system science education and research

Ramamurthy

2006

Adv. Geosci.

View full text Add to dashboard Cite

Abstract.A revolution is underway in the role played by cyberinfrastructure and modern data services in the conduct of research and education. We live in an era of an unprecedented data volume from diverse sources, multidisciplinary analysis and synthesis, and active, learner-centered education emphasis. Complex environmental problems such as global change and water cycle transcend disciplinary and geographic boundaries, and their solution requires integrated earth system science approaches. Contemporary education strategies recommend adopting an Earth system science approach for teaching the geosciences, employing pedagogical techniques such as enquiry-based learning. The resulting transformation in geoscience education and research creates new opportunities for advancement and poses many challenges. The success of the scientific enterprise depends heavily on the availability of a state-of-the-art, robust, and flexible cyberinfrastructure, and on the timely access to quality data, products, and tools to process, manage, analyze, integrate, publish, and visualize those data.Concomittantly, rapid advances in computing, communication, and information technologies have revolutionized the provision and use of data, tools and services. The profound consequences of Moore's Law and the explosive growth of the Internet are well known. On the other hand, how other technological trends have shaped the development of data services is less well understood. For example, the advent of digital libraries, web services, open standards and protocols have been important factors in shaping a new generation of cyberinfrastructure for solving key scientific and educational problems. This paper presents a broad overview of these issues, along with a survey of key information technology trends, and discuses how those trends are enabling new approaches to applying data services for solving geoscientific problems.

show abstract

“…The Karma provenance framework [3] is used in the LEAD Cyberinfrastructure [4,5] project to collect and query over provenance generated from meso-scale weather forecasting workflows.…”

Section: Introductionmentioning

confidence: 99%

Query capabilities of the Karma provenance framework

Simmhan¹,

Plale²,

Gannon³

2007

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SUMMARYProvenance metadata in e-Science captures the derivation history of data products generated from scientific workflows. Provenance forms a glue linking workflow execution with associated data products, and finds use in determining the quality of derived data, tracking resource usage, and for verifying and validating scientific experiments. In this article, we discuss the scope of provenance collected in the Karma provenance framework used in the LEAD Cyberinfrastructure project, distinguishing provenance metadata from generic annotations. We further describe our approaches to querying for different forms of provenance in Karma in the context of queries in the first provenance challenge. We use an incremental, building-block method to construct provenance queries based on the fundamental querying capabilities provided by the Karma service centered on the provenance data model. This has the advantage of keeping the Karma service generic and simple, and yet supports a wide range of queries. Karma successfully answers all but one challenge query.

show abstract

Service Oriented Architectures for Science Gateways on Grid Systems

Cited by 24 publications

References 11 publications

High‐performance computing: to boldly go where no human has gone before

High‐performance computing: to boldly go where no human has gone before

A new generation of cyberinfrastructure and data services for earth system science education and research

Query capabilities of the Karma provenance framework

Contact Info

Product

Resources

About