4,028 research outputs found
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
Practical applications of multi-agent systems in electric power systems
The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur
Enabling e-Research in combustion research community
Abstract
This paper proposes an application of the Collaborative e-Science Architecture (CeSA) to enable e-Research in combustion research community. A major problem of the community is that data required for constructing modelling might already exist but scattered and improperly evaluated. That makes the collection of data for constructing models difficult and time-consuming. The decentralised P2P collaborative environment of the CeSA is well suited to solve this distributed problem. It opens up access to scattered data and turns them to valuable resources. Other issues of the community addressed here are the needs for computational resources, storages and interoperability amongst different data formats can also be addressed by the use of Grid environment in the CeSA
Analysis of current middleware used in peer-to-peer and grid implementations for enhancement by catallactic mechanisms
This deliverable describes the work done in task 3.1, Middleware analysis: Analysis of current middleware used in peer-to-peer and grid implementations for enhancement by catallactic mechanisms from work package 3, Middleware Implementation. The document is divided in four parts: The introduction with application scenarios and middleware requirements, Catnets middleware architecture, evaluation of existing middleware toolkits, and conclusions. -- Die Arbeit definiert Anforderungen an Grid und Peer-to-Peer Middleware Architekturen und analysiert diese auf ihre Eignung für die prototypische Umsetzung der Katallaxie. Eine Middleware-Architektur für die Umsetzung der Katallaxie in Application Layer Netzwerken wird vorgestellt.Grid Computing
An E-Learning Semantic Grid for Life science Education
There are a lot of life science databases and services on the Internet nowadays, especially in life science e-science. In this paper, we will present an E-Learning Semantic Grid that integrates these resources provided by both teachers and scientists for life science education. It uses domain ontologies to integrate these heterogeneous life science database and service resources, and supports ontology-based e-learning data-sharing and service-coordination for life science teachers and students in an e-learning virtual organization. Our system provides life science students with semantically superior experience in learning activities, and also extends the function of life science e-science. It has a promising future in the domain of life science education
Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System
Resource Discovery (RD) is a key issue in Internet-based distributed sytems such as
grid. RD is about locating an appropriate resource/service type that matches the user's
application requirements. This is very important, as resource reservation and task
scheduling are based on it. Unfortunately, RD in grid is very challenging as resources
and users are distributed, resources are heterogeneous in their platforms, status of the
resources is dynamic (resources can join or leave the system without any prior notice)
and most recently the introduction of a new type of grid called intergrid (grid of grids)
with the use of multi middlewares. Such situation requires an RD system that has rich
interoperability, scalability, decentralization and dynamism features. However,
existing grid RD systems have difficulties to attain these features. Not only that, they
lack the review and evaluation studies, which may highlight the gap in achieving the
required features. Therefore, this work discusses the problem associated with intergrid
RD from two perspectives. First, reviewing and classifying the current grid RD
systems in such a way that may be useful for discussing and comparing them. Second,
propose a novel RD framework that has the aforementioned required RD features. In
the former, we mainly focus on the studies that aim to achieve interoperability in the
first place, which are known as RD systems that use semantic information (semantic
technology). In particular, we classify such systems based on their qualitative use of
the semantic information. We evaluate the classified studies based on their degree of
accomplishment of interoperability and the other RD requirements, and draw the
future research direction of this field. Meanwhile in the latter, we name the new
framework as semantic-based scalable decentralized dynamic RD. The framework
further contains two main components which are service description, and service
registration and discovery models. The earlier consists of a set of ontologies and
services. Ontologies are used as a data model for service description, whereas the
services are to accomplish the description process. The service registration is also based on ontology, where nodes of the service (service providers) are classified to
some classes according to the ontology concepts, which means each class represents a
concept in the ontology. Each class has a head, which is elected among its own class
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nodes/members. Head plays the role of a registry in its class and communicates with
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the other heads of the classes in a peer to peer manner during the discovery process.
We further introduce two intelligent agents to automate the discovery process which
are Request Agent (RA) and Description Agent (DA). Eaclj. node is supposed to have
both agents. DA describes the service capabilities based on the ontology, and RA
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carries the service requests based on the ontology as well. We design a service search
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algorithm for the RA that starts the service look up from the class of request origin
first, then to the other classes.
We finally evaluate the performance of our framework ~ith extensive simulation
experiments, the result of which confirms the effectiveness of the proposed system in
satisfying the required RD features (interoperability, scalability, decentralization and
dynamism). In short, our main contributions are outlined new key taxonomy for the
semantic-based grid RD studies; an interoperable semantic description RD component
model for intergrid services metadata representation; a semantic distributed registry
architecture for indexing service metadata; and an agent-qased service search and
selection algorithm.
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Is a Semantic Web Agent a Knowledge-Savvy Agent?
The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike
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