1,347 research outputs found
Mobile Computing in Physics Analysis - An Indicator for eScience
This paper presents the design and implementation of a Grid-enabled physics
analysis environment for handheld and other resource-limited computing devices
as one example of the use of mobile devices in eScience. Handheld devices offer
great potential because they provide ubiquitous access to data and
round-the-clock connectivity over wireless links. Our solution aims to provide
users of handheld devices the capability to launch heavy computational tasks on
computational and data Grids, monitor the jobs status during execution, and
retrieve results after job completion. Users carry their jobs on their handheld
devices in the form of executables (and associated libraries). Users can
transparently view the status of their jobs and get back their outputs without
having to know where they are being executed. In this way, our system is able
to act as a high-throughput computing environment where devices ranging from
powerful desktop machines to small handhelds can employ the power of the Grid.
The results shown in this paper are readily applicable to the wider eScience
community.Comment: 8 pages, 7 figures. Presented at the 3rd Int Conf on Mobile Computing
& Ubiquitous Networking (ICMU06. London October 200
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
Some Pattern Recognition Challenges in Data-Intensive Astronomy
We review some of the recent developments and challenges posed by the data
analysis in modern digital sky surveys, which are representative of the
information-rich astronomy in the context of Virtual Observatory. Illustrative
examples include the problems of an automated star-galaxy classification in
complex and heterogeneous panoramic imaging data sets, and an automated,
iterative, dynamical classification of transient events detected in synoptic
sky surveys. These problems offer good opportunities for productive
collaborations between astronomers and applied computer scientists and
statisticians, and are representative of the kind of challenges now present in
all data-intensive fields. We discuss briefly some emergent types of scalable
scientific data analysis systems with a broad applicability.Comment: 8 pages, compressed pdf file, figures downgraded in quality in order
to match the arXiv size limi
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
DataONE: Facilitating eScience through Collaboration
Objective: To introduce DataONE, a multi-institutional, multinational, and interdisciplinary collaboration that is developing the cyberinfrastructure and organizational structure to support the full information lifecycle of biological, ecological, and environmental data and tools to be used by researchers, educators, and the public at large.
Setting: The dynamic world of data intensive science at the point it interacts with the grand challenges facing environmental sciences.
Methods: Briefly discuss science’s “fourth paradigm,” then introduce how DataONE is being developed to answer the challenges presented by this new environment. Sociocultural perspectives are the primary focus of the discussion.
Results: DataONE is highly collaborative. This is a result of its cyberinfrastructure architecture, its interdisciplinary nature, and its organizational diversity. The organizational structure of an agile management team, diverse leadership team, and productive working groups provides for a successful collaborative environment where substantial contributions to the DataONE mission have been made by a large number of people.
Conclusions: Librarians and information science researchers are key partners in the development of DataONE. These roles are likely to grow as more scientists engage data at all points of the data lifecycle
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