10,455 research outputs found
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
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
Report of the user requirements and web based access for eResearch workshops
The User Requirements and Web Based Access for eResearch Workshop, organized jointly by NeSC and NCeSS, was held on 19 May 2006. The aim was to identify lessons learned from e-Science projects that would contribute to our capacity to make Grid infrastructures and tools usable and accessible for diverse user communities. Its focus was on providing an opportunity for a pragmatic discussion between e-Science end users
and tool builders in order to understand usability challenges, technological options, community-specific content and needs, and methodologies for design and development. We invited members of six UK e-Science projects and one US project, trying as far as
possible to pair a user and developer from each project in order to discuss their contrasting perspectives and experiences. Three breakout group sessions covered the
topics of user-developer relations, commodification, and functionality. There was also extensive post-meeting discussion, summarized here.
Additional information on the workshop, including the agenda, participant list, and talk slides, can be found online at http://www.nesc.ac.uk/esi/events/685/
Reference: NeSC report UKeS-2006-07 available from http://www.nesc.ac.uk/technical_papers/UKeS-2006-07.pd
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Scalable grid resource allocation for scientific workflows using hybrid metaheuristics
Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid
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