38,106 research outputs found
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
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
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
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
Querying Large Physics Data Sets Over an Information Grid
Optimising use of the Web (WWW) for LHC data analysis is a complex problem
and illustrates the challenges arising from the integration of and computation
across massive amounts of information distributed worldwide. Finding the right
piece of information can, at times, be extremely time-consuming, if not
impossible. So-called Grids have been proposed to facilitate LHC computing and
many groups have embarked on studies of data replication, data migration and
networking philosophies. Other aspects such as the role of 'middleware' for
Grids are emerging as requiring research. This paper positions the need for
appropriate middleware that enables users to resolve physics queries across
massive data sets. It identifies the role of meta-data for query resolution and
the importance of Information Grids for high-energy physics analysis rather
than just Computational or Data Grids. This paper identifies software that is
being implemented at CERN to enable the querying of very large collaborating
HEP data-sets, initially being employed for the construction of CMS detectors.Comment: 4 pages, 3 figure
Development of Grid e-Infrastructure in South-Eastern Europe
Over the period of 6 years and three phases, the SEE-GRID programme has
established a strong regional human network in the area of distributed
scientific computing and has set up a powerful regional Grid infrastructure. It
attracted a number of user communities and applications from diverse fields
from countries throughout the South-Eastern Europe. From the infrastructure
point view, the first project phase has established a pilot Grid infrastructure
with more than 20 resource centers in 11 countries. During the subsequent two
phases of the project, the infrastructure has grown to currently 55 resource
centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16
participating countries. Inclusion of new resource centers to the existing
infrastructure, as well as a support to new user communities, has demanded
setup of regionally distributed core services, development of new monitoring
and operational tools, and close collaboration of all partner institution in
managing such a complex infrastructure. In this paper we give an overview of
the development and current status of SEE-GRID regional infrastructure and
describe its transition to the NGI-based Grid model in EGI, with the strong SEE
regional collaboration.Comment: 22 pages, 12 figures, 4 table
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