78,161 research outputs found
The Gridbus Toolkit for Service Oriented Grid and Utility Computing: An Overview and Status Report
Grids aim at exploiting synergies that result from cooperation of autonomous
distributed entities. The synergies that result from grid cooperation include
the sharing, exchange, selection, and aggregation of geographically distributed
resources such as computers, data bases, software, and scientific instruments
for solving large-scale problems in science, engineering, and commerce. For
this cooperation to be sustainable, participants need to have economic
incentive. Therefore, "incentive" mechanisms should be considered as one of key
design parameters of Grid architectures. In this article, we present an
overview and status of an open source Grid toolkit, called Gridbus, whose
architecture is fundamentally driven by the requirements of Grid economy.
Gridbus technologies provide services for both computational and data grids
that power the emerging eScience and eBusiness applications.Comment: 11 pages, 3 figures, 3 table
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
Image processing methods and architectures in diagnostic pathology.
Grid technology has enabled the clustering and the efficient and secure access to and interaction among a wide variety of geographically distributed resources such as: supercomputers, storage systems, data sources, instruments and special devices and services. Their main applications include large-scale computational and data intensive problems in science and engineering. General grid structures and methodologies for both software and hardware in image analysis for virtual tissue-based diagnosis has been considered in this paper. This methods are focus on the user level middleware. The article describes the distributed programming system developed by the authors for virtual slide analysis in diagnostic pathology. The system supports different image analysis operations commonly done in anatomical pathology and it takes into account secured aspects and specialized infrastructures with high level services designed to meet application requirements. Grids are likely to have a deep impact on health related applications, and therefore they seem to be suitable for tissue-based diagnosis too. The implemented system is a joint application that mixes both Web and Grid Service Architecture around a distributed architecture for image processing. It has shown to be a successful solution to analyze a big and heterogeneous group of histological images under architecture of massively parallel processors using message passing and non-shared memory
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