Grid Computing Technologies Research Articles

Toward an integrated framework for geosensor grid

Grid computing is deemed as a good solution to the digital earth infrastructure. Various geographically dispersed geospatial resources can be connected and merged into a ‘supercomputer’ by using the grid-computing technology. On the other side, geosensor networks offer a new perspective for collecting physical data dynamically and modeling a real-time virtual world. Integrating geosensor networks and grid computing in geosensor grid can be compared to equipping the geospatial information grid with ‘eyes’ and ‘ears.’ Thus, real-time information in the physical world can be processed, correlated, and modeled to enable complex and advanced geospatial analyses on geosensor grid with capability of high-performance computation. There are several issues and challenges that need to be overcome before geosensor grid comes true. In this paper, we propose an integrated framework, comprising the geosensor network layer, the grid layer and the application layer, to address these design issues. Key technologies of the geosensor grid framework are discussed. And, a geosensor grid testbed is set up to illustrate the proposed framework and improve our geosensor grid design.

Implementation Methods for Estimating Haplotypes with Grid Computing Technology

Introduction: PHASE is a software for haplotype reconstruction and recombination rate estimation from population data and implements the methods for estimating haplotypes from population genotype data. This software require enormous computing power due to the complexity algorithms used. Recent advancement in grid computing and multi-core processing technology has resulted in the creating of virtual supercomputers (that are capable of massive parallel computations). PHASE version 2.0 was downloaded from PHASE’s homepage (http://stephenslab.uchicago.edu/software.html), and installed on 10 Apple iMac computers (20 CPUs) running Mac OS 10.5 using Apple™ Xserve as a controller. A PHP web-based application was developed to enable easy job submission and result retrieval. Haplotype estimation jobs for PHASE were submitted using a web browser running either from Windows®, Linux® or Macintosh™ OS 10.5 operating systems. Jobs consisting of 20 input files of genomic loci (SNPs) were submitted to the Xgrid via the controller and the job completion time for each run were recorded and compared. The job completion time for estimating 20 input files and returning the results to the controller using XGrid (20 CPUs) took 4,980 seconds compared with 58,800 seconds using a single computer (2 CPUs), representing 91.5% reduction in computing time.

Implementation Methods for Estimating Haplotypes with Grid Computing Technology

AbstractIntroduction: PHASE is a software for haplotype reconstruction and recombination rate estimation from population data and implements the methods for estimating haplotypes from population genotype data. This software require enormous computing power due to the complexity algorithms used. Recent advancement in grid computing and multi-core processing technology has resulted in the creating of virtual supercomputers (that are capable of massive parallel computations). Method: PHASE version 2.0 was downloaded from PHASE’s homepage (http://stephenslab.uchicago.edu/software.html), and installed on 10 Apple iMac computers (20 CPUs) running Mac OS 10.5 using Apple™ Xserve as a controller. A PHP web-based application was developed to enable easy job submission and result retrieval. Haplotype estimation jobs for PHASE were submitted using a web browser running either from Windows®, Linux® or Macintosh™ OS 10.5 operating systems. Jobs consisting of 20 input files of genomic loci (SNPs) were submitted to the Xgrid via the controller and the job completion time for each run were recorded and compared. Results: The job completion time for estimating 20 input files and returning the results to the controller using XGrid (20 CPUs) took 4,980 seconds compared with 58,800 seconds using a single computer (2 CPUs), representing 91.5% reduction in computing time.

System-level virtualization research at Oak Ridge National Laboratory

System-level virtualization is today enjoying a rebirth as a technique to effectively share what had been considered large computing resources which subsequently faded from the spotlight as individual workstations gained in popularity with a “one machine–one user” approach. One reason for this resurgence is that the simple workstation has grown in capability to rival anything similar, available in the past. Thus, computing centers are again looking at the price/performance benefit of sharing that single computing box via server consolidation. However, industry is only concentrating on the benefits of using virtualization for server consolidation (enterprise computing) whereas our interest is in leveraging virtualization to advance high-performance computing (HPC). While these two interests may appear to be orthogonal, one consolidating multiple applications and users on a single machine while the other requires all the power from many machines to be dedicated solely to its purpose, we propose that virtualization does provide attractive capabilities that may be exploited to the benefit of HPC interests. This does raise the two fundamental questions: is the concept of virtualization (a machine “sharing” technology) really suitable for HPC and if so, how does one go about leveraging these virtualization capabilities for the benefit of HPC. To address these questions, this document presents ongoing studies on the usage of system-level virtualization in a HPC context. These studies include an analysis of the benefits of system-level virtualization for HPC, a presentation of research efforts based on virtualization for system availability, and a presentation of research efforts for the management of virtual systems. The basis for this document was the material presented by Stephen L. Scott at the Collaborative and Grid Computing Technologies meeting held in Cancun, Mexico on April 12–14, 2007.

Proposal of Vibration Table in an Extended World by Grid Computing Technology for Assembled Structures

Proposal of Vibration Table in an Extended World by Grid Computing Technology for Assembled Structures

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at <igrid-ext.cryst.bbk.ac.uk/immunogrid>.

Carbon-centric computing - IT solutions for climate change

IT has a role to play in the current debate on climate change. The current discourse on IT and climate change views IT in a negative light, as a polluter. What remains unrecognized is the critical role of IT as a source of solutions to the climate change problem. We live in a massive, inter-connected Planet Earth Supply Chain. IT provides a range of tools to model, manage and optimize this supply chain. The University of Wollongong Carbon-Centric Computing Initiative (CCCI) seeks to seed a program of research that addresses the climate change problem with a range of computing technologies including (but not limited to): optimization technologies, supply chain management technologies, business process management/process improvement technologies, grid computing (e.g., utility grid) and virtualization technologies, ICT-enabled conferencing and collaboration technologies as well as ICT for knowledge sharing and network-centric advocacy. The contours of this new and exciting space for research and industry development are described in this report authored by three University of Wollongong academics: Prof. Aditya Ghose (Director, Decision Systems Lab, School of Computer Science and Software Engineering), A/Prof. Helen Hasan (Director, ATUL, School of Economics) and Prof. Trevor Spedding (Head, School of Management and Marketing). The report provides insights into a set of representative points within this new space. It describes how existing web infrastructure could be leveraged to devise the optimizing web — a massive, globally inter-connected network of optimizers helping support decisions that would reduce the global carbon footprint. It describes how computer simulation models can provide the basis for sustainable manufacturing and environmental management in the enterprise. It describes how IT based techniques can help support supply chain optimization audits to determine if and how value might be best derived from the judicious use of optimization technology. It describes the critical role ICT-enabled collaboration technologies can play in reducing the carbon footprint. It also addresses the key role ICT-based knowledge sharing and network-centric advocacy can play in obtaining broader social engagement in this debate. The report addresses the policy dimension to these issues and the need for an industry-academia consortium to drive such an agenda forward. Disciplines Physical Sciences and Mathematics Publication Details This report was originally published as Ghose, A, Hasan, H and Spedding, T, Carbon-centric Computing: IT Solutions for Climate Change A report prepared by the University Working Group on the Carbon-centric Computing Initiative, University of Wollongong, 2008, 17p. This report is available at Research Online: http://ro.uow.edu.au/infopapers/669 IT SOLUTIONS FO R CLIMATE CHANG E A report prepared by the University of Wollongong Working Group on the Carbon-Centric Computing Initiative Professor Aditya Ghose Director, Decision Systems Lab School of Computer Science and Software Engineering Associate Professor Helen Hasan Director, ATUL School of Economics Professor Trevor Spedding Head, School of Management and Marketing Contact Us Professor Aditya Ghose School of Computer Science and Software Engineering, Faculty of Informatics aditya@uow.edu.au http://www.uow.edu.au/~aditya/ Associate Professor Helen Hasan School of Economics, Faculty of Commerce hasan@uow.edu.au http://uow.edu.au/~hasan/ Professor Trevor Spedding Head, School of Management and Marketing, Faculty of Commerce spedding@uow.edu.au For more publications of the authors of the report see: http://ro.uow.edu.au/ Carbon-Centric Computing: IT Solutions for Climate Change A report prepared by the University of Wollongong Working Group on the Carbon-Centric Computing Initiative Professor Aditya Ghose School of Computer Science and Software Engineering, Faculty of Informatics Associate Professor Helen Hasan School of Economics, Faculty of Commerce Professor Trevor Spedding Head, School of Management and Marketing, Faculty of Commerce Researchers: Professor Aditya Ghose Associate Professor Helen Hasan Professor Trevor Spedding First published 2008 Copyright © the authors, 2008 All rights reserved. Except under the conditions described in the Coypright Act 1968 of Australia and subsequent amendments, no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, withoug the prior prermission of the copyright owners.

Design Principles and IT Overviews of the GEO Grid

As the Earth's ecosystem is a spatially and temporally complex system by nature, it is not sufficient to observe such events and phenomena locally; problems must be solved on a global scale. Therefore, the accumulation of knowledge about the Earth in various forms and a scientifically correct understanding of the Earth are necessary. The authors have been leading the ldquoGlobal Earth Observation (GEO) Gridrdquo project since 2005, which is primarily aimed at providing an e-Science infrastructure for the worldwide Earth sciences community. In the community, there are wide varieties of existing datasets including satellite imagery, geological data, and ground sensed data that each data owner insists own licensing policy. Also, there are so many related projects that will be configured as a virtual organization (VO) enabled by Grid technology. The GEO Grid is designed to integrate all of the relevant data virtually, again enabled by Grid technology, and is accessible as a set of services. In this paper, first we describe design principles of the GEO Grid that are determined based on accommodating users requirements for publishing, managing, and using data. Second, software architecture and its preliminary implementations are specified where we take the Grid computing and Web service technologies as the core components that comply with a standard set of technologies and protocols. In addition, GEO Grid has been recognized to contribute to GEO or Global Earth Observation System of Systems (GEOSS) as a part of the Japanese government's commitment.

Grid Computing and Qualitative Social Science

Qualitative research is increasingly important in policy-related and applied work, as well as in academic work. Grid and high-performance computing (HPC) technologies promise significant potential returns for qualitative researchers. Tagged cyber-research in the United States and e-social science in the United Kingdom (and e-research in general), the application of HPC technologies can enhance the scope, depth, and rigor of qualitative inquiry by enabling new data-handling capacities and analytic procedures; new support for work with colleagues based elsewhere; and new facilities to archive, curate, and exploit the many kinds of data that qualitative researchers use. From these resources flow new challenges to conventions of privacy and research ethics, data integrity and data protection, and the relations between scientific communities and society. Based on a survey, individual interviews, and group discussions, involving qualitative researchers and computer scientists, this article scans existing applications of grid and HPC technologies to qualitative research; indicates potential applications; and identifies associated ethical, practical, and technological challenges.

Payment and negotiation for the next generation Grid and Web

AbstractWe present a proposal for a next‐generation Internet based on chargeable Web Services and Utility Computing realized by a series of open but interacting markets. We demonstrate through the U.K. e‐Science project ‘A Market for Computational Services’ the development of some of the fundamental building blocks for such a Grid computational marketplace. This paper describes the motivation behind this restructuring of the Internet and Web‐based activities as a series of markets and how Grid Computing technologies can contribute towards this goal. The paper details the work undertaken at the London e‐Science Centre to build a framework to create and support negotiable and chargeable Web Services. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Design and Evaluation of Web-Based Medical Imaging Distribution System with Intelligence Sharing Environment in Wide Area Network Using Grid Computing Technology

Design and Evaluation of Web-Based Medical Imaging Distribution System with Intelligence Sharing Environment in Wide Area Network Using Grid Computing Technology

The Saverbeans Screensaver and Initium RJS System Integration: Part 5 By.

This paper describes the integration of our Java-based screensaver framework with our Initium Remote Job Submission (IRJS) grid computing middleware. Initium RJS is a Java Web Start (JAWS) based grid-computing technology. This is part 5 of a 5 part series. In parts 1, 2, and 3, we described screensavers for MS Windows, Xwindows, and the Macintosh. Part 4, describe automatic deployment of the screensavers into the MS Windows and XWindows systems. This paper introduces the use of our grid system. We provide an example that shows how to take a von Neumann style program, break it up, and deploy it to our grid system.

Distributed MDSplus database performance with Linux clusters

The staff at the DIII-D National Fusion Facility, operated for the USDOE by General Atomics, are investigating the use of grid computing and Linux technology to improve performance in our core data management services. We are in the process of converting much of our functionality to cluster-based and grid-enabled software. One of the most important pieces is a new distributed version of the MDSplus scientific data management system that is presently used to support fusion research in over 30 countries worldwide. To improve data handling performance, the staff is investigating the use of Linux clusters for both data clients and servers. The new distributed capability will result in better load balancing between these clients and servers, and more efficient use of network resources resulting in improved support of the data analysis needs of the scientific staff.

Q&amp;A with John Darley

Regarding the upcoming 2006 Intelligent Energy conference, why the term "intelligent energy"? The provision of energy to meet the projected global economic growth in the 21st century will require our industry to continuously improve effectiveness in all areas of activity, from initial exploration to maximizing the ultimate economic recovery while incorporating necessary environmental measures. We need to adopt ideas and technologies—often information-technology (IT) related technologies, and sometimes from other industries—to achieve this goal. The term "intelligent energy" captures the concept of a new approach, harnessing new IT-enabled capability, to respond to the energy challenge. What do you hope attendees take away from this conference? First, we expect that participants in the conference will gain a good understanding of new technologies, which are currently being applied in the intelligent energy arena, to improve field performance. There are great examples of leading-edge applications delivering real value today, and the conference will promote the sharing of these experiences. Second, we hope that delegates will also appreciate that the intelligent energy field is not simply a question of applying new tools and techniques. The real value is to be gained by changing the way we work, by more effective integration across various activities, by using real-time data in decision making, etc. Exciting ideas are still emerging in these areas, and we look for an active dialogue between participants to move the industry forward. And, third, our aspiration is that the conference will help delegates, and their respective parent organizations, to build momentum to take the intelligent energy concepts forward with pace and determination. So far, we see ad hoc examples of successful applications. Real value will accrue to the industry with more wide-scale application and uptake of many of the ideas and technologies. How well is the industry doing in adapting digital-age technologies to the oil field? In some areas, the industry takes a leading-edge role in the development or application of new digital capabilities. Examples would be the use of advanced telecommunications to facilitate global communications and control systems (e.g., the real-time operating centers used by many companies to monitor, control, and optimize drilling activity), or the use of powerful parallel-processing and grid-computing technologies to process seismic data. At the other extreme, the opportunity to fully integrate oilfield activities, using IT capabilities in such areas as real-time data management, inventory control, and production optimization, is only sporadically applied, and there is scope for both broader and accelerated application of digital technology. What is the industry struggling with in adapting or implementing these technologies? The key challenge appears to be the integration of digital technology across the different work flows and areas of activity in the oil and gas industry. While individual applications are often successful, the ability to integrate data flows from the subsurface, through reservoir and well-production monitoring, and into surface control systems still proves a challenge in practice.

An e-Learning Platform Based on Grid Architecture

The overall popularity of the Internet has helped e-learning become a hot method for learning in recent years. Over the Internet, learners can freely absorb new knowledge without restrictions on time or place. Many companies have adopted e-learning to train their employees. An e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. E-learning has been shown to have impressive potential in e-commerce. At present, most e-learning environment architectures use single computers or servers as their structural foundations. As soon as their work loads increase, their software and hardware must be updated or renewed. This is a big burden on organizations that lack sufficient funds. Thus, in this study we employ a kind of Grid Computing technology, called the ”Data Grid” to integrate idle computer resources in enterprises into e-learning platforms, thus eliminating the need to purchase costly high-level servers and other equipment.

Towards performance control on the Grid

Advances in computational Grid technologies are enabling the development of simulations of complex biological and physical systems. Such simulations can be assembled from separate components--separately deployable computation units of well-defined functionality. Such an assemblage can represent an application composed of interacting simulations or might comprise multiple instances of a simulation executing together, each running with different simulation parameters. However, such assemblages need the ability to cope with heterogeneous and dynamically changing execution environments, particularly where such changes can affect performance. This paper describes the design and implementation of a prototype performance control system (PerCo), which is capable of monitoring the progress of simulations and redeploying them so as to optimize performance. The ability to control performance by redeployment is demonstrated using an assemblage of lattice Boltzmann simulations running with and without control policies. The cost of using PerCo is evaluated and it is shown that PerCo is able to reduce overall execution time.

The Integration of Grid-enabled Internet GIServices and Geographic Semantic Web Technologies

This paper presents a new framework for Grid-enabled GIService web portals to facilitate the building of high-level intelligent Internet GIServices. The five-tier architecture suggested here can support advanced semantic search and query functions for distributed GIServices by combining Grid computing, Semantic Web, and software agent technologies. The design of the web portal user interface with software agents can help end users combine and integrate computing power with geospatial data and services. Geospatial ontologies are incorporated into the framework by using geographic web ontology language (G-OWL). Intelligent software agent (GeoAgent) techniques are used to automate the procedures for searching, retrieving and processing geospatial data. The proposed Internet GIService architecture will provide a blue print for the establishment of Grid-enabled Internet GIServices and will help the GIS community to identify potential technical challenges to implement intelligent Internet GIServices.

Grid computing and collaboration technology in support of fusion energy sciences

Science research in general and magnetic fusion research in particular continue to grow in size and complexity resulting in a concurrent growth in collaborations between experimental sites and laboratories worldwide. The simultaneous increase in wide area network speeds has made it practical to envision distributed working environments that are as productive as traditionally collocated work. In computing power, it has become reasonable to decouple production and consumption resulting in the ability to construct computing grids in a similar manner as the electrical power grid. Grid computing, the secure integration of computer systems over high speed networks to provide on-demand access to data analysis capabilities and related functions, is being deployed as an alternative to traditional resource sharing among institutions. For human interaction, advanced collaborative environments are being researched and deployed to have distributed group work that is as productive as traditional meetings. The DOE Scientific Discovery through Advanced Computing Program initiative has sponsored several collaboratory projects, including the National Fusion Collaboratory Project, to utilize recent advances in grid computing and advanced collaborative environments to further research in several specific scientific domains. For fusion, the collaborative technology being deployed is being used in present day research and is also scalable to future research, in particular, to the International Thermonuclear Experimental Reactor experiment that will require extensive collaboration capability worldwide. This paper briefly reviews the concepts of grid computing and advanced collaborative environments and gives specific examples of how these technologies are being used in fusion research today.

Aviators, Moguls, Fashionistas and Barons: Economics and Ownership in Second Life

Second Life is a digital world that relies on a unique combination of grid computing and streaming technology [Rosedale03] to enable virtually all of its content to be created by its residents. To maximize the quality and quantity of user-created content, Second Life has embraced strong economic and legal connections to the real world. This approach is quite different than conventional massively multiplayer online games (MMOGs). Since Second Life launched in June of 2003, significant changes have been made to the business model and internal economic structure. These changes have shaped the many approaches residents have taken to creating content, building experiences and making real-world profits. This Article will discuss the evolution of Second Life's business model and internal economy, its entrepreneurial activities, and the impact of those activities on Second Life's residents and community.

A Combined Fuzzy-Neural Network Model for Non-Linear Prediction of 3-D Rendering Workload in Grid Computing

Implementation of a commercial application to a grid infrastructure introduces new challenges in managing the quality-of-service (QoS) requirements, most stem from the fact that negotiation on QoS between the user and the service provider should strictly be satisfied. An interesting commercial application with a wide impact on a variety of fields, which can benefit from the computational grid technologies, is three-dimensional (3-D) rendering. In order to implement, however, 3-D rendering to a grid infrastructure, we should develop appropriate scheduling and resource allocation mechanisms so that the negotiated (QoS) requirements are met. Efficient scheduling schemes require modeling and prediction of rendering workload. In this paper workload prediction is addressed based on a combined fuzzy classification and neural network model. Initially, appropriate descriptors are extracted to represent the synthetic world. The descriptors are obtained by parsing RIB formatted files, which provides a general structure for describing computer-generated images. Fuzzy classification is used for organizing rendering descriptor so that a reliable representation is accomplished which increases the prediction accuracy. Neural network performs workload prediction by modeling the nonlinear input-output relationship between rendering descriptors and the respective computational complexity. To increase prediction accuracy, a constructive algorithm is adopted in this paper to train the neural network so that network weights and size are simultaneously estimated. Then, a grid scheduler scheme is proposed to estimate the queuing order that the tasks should be executed and the most appopriate processor assignment so that the demanded QoS are satisfied as much as possible. A fair scheduling policy is considered as the most appropriate. Experimental results on a real grid infrastructure are presented to illustrate the efficiency of the proposed workload prediction--scheduling algorithm compared to other approaches presented in the literature.