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The grid is a promising infrastructure that can allow scientists and engineers to access resources among geographically distributed environments. Grid computing is a new technology which focuses on aggregating resources (e.g., processor cycles, disk storage, and contents) from a large-scale computing platform. Making grid computing a reality requires a resource broker to manage and monitor available resources. This paper presents a workflow-based resource broker whose main functions are matching available resources with user requests and considering network information statuses during matchmaking in computational grids. The resource broker provides a graphic user interface for accessing available and the appropriate resources via user credentials. This broker uses the Ganglia and NWS tools to monitor resource status and network-related information, respectively. Then we propose a history-based execution time estimation model to predict the execution time of parallel applications, according to previous execution results. The experimental results show that our model can accurately predict the execution time of embarrassingly parallel applications. We also report on using the Globus Toolkit to construct a grid platform called the TIGER project that integrates resources distributed across five universities in Taichung city, Taiwan, where the resource broker was developed. [ABSTRACT FROM AUTHOR]

The article presents a model for making computation offloading decisions in computational grid settings in which schedulers determine when to move parts of a computation to more capable resources to improve performance. The framework unifies decision models by formulating the problem as a statistical decision problem that can either be treated classically or using a Bayesian approach. Moreover, it examines the efficacy of several different decision strategies employed by previous systems. It concludes that a Bayesian approach employing automatic change-point detection when estimating the prior distribution is the best performing approach.

This paper describes a protein tertiary structure prediction service implemented in a Grid Environment. The service has been used for predicting the dicarboxylate carrier (DIC) of Saccharomyces cerevisiae by using the homology modelling approach. The visualization of the predicted model is made possible by using an interactive virtual reality environment based on X3D and Ajax3d technologies. [ABSTRACT FROM AUTHOR]

A sustainable market-like computational grid has two characteristics: it must allow resource providers and resource consumers to make autonomous scheduling decisions, and both parties of providers and consumers must have sufficient incentives to stay and play in the market. In this paper, we formulate this intuition of optimizing incentives for both parties as a dual-objective scheduling problem. The two objectives identified are to maximize the success rate of job execution and to minimize fairness deviation among resources. The challenge is to develop a grid scheduling scheme that enables individual participants to make autonomous decisions while producing a desirable emergent property in the grid system; that is, the two systemwide objectives are achieved simultaneously. We present an incentive-based scheduling scheme, which utilizes a peer-to-peer decentralized scheduling framework, a set of local heuristic algorithms, and three market instruments of job announcement, price, and competition degree. The performance of this scheme is evaluated via extensive simulation using synthetic and real workloads. The results show that our approach outperforms other scheduling schemes in optimizing incentives for both consumers and providers, leading to highly successful job execution and fair profit allocation. [ABSTRACT FROM AUTHOR]

The rapid advances of network technologies shed light on many aspects of the practicability of large scale ubiquitous computing. Grid technology has been recognized as an efficient solution to coordinate large-scale shared resources and execute complex applications in heterogeneous network environments. The problem of resource management and task allocation has always been one of the main challenges. In this paper, we present an efficient task allocation strategy for distributing tasks onto computing nodes in the underlying heterogeneous networks. The contribution of the proposed technique is to minimize average turnaround time by dispatching tasks to processors with smallest communication ratio. System throughput could be also enhanced by dispersing processor idle time. The proposed technique can be applied to heterogeneous cluster systems as well as computational grid environments, in which the communication costs vary in different clusters. Experimental results show that the proposed scheme outperforms other previous algorithms in terms of throughput and turnaround time. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a multi-level scheduling algorithm for global optimization in grid computing. This algorithm provides a global optimization through a cross-layer optimization realized by decomposing the optimization problem in different sub-problems each of them corresponding to one among the grid layers such as application layer, collective layer and fabric layer. The QoS of an abstraction level is a utility function that assigns at every level a different value and that depends on the kind of task that is executed on the grid. The global QoS is given by processing of the utility function values of the three different levels, using the Lagrangian method. Multi-level QoS scheduling algorithm is evaluated in terms of system efficiency and their economic efficiency, respectively. Economic efficiency includes user utility, service provider’s revenue and grid global utility. System efficiency includes execution success ratio and resource allocation ratio. [Copyright &y& Elsevier]

We present a Grid portal problem (which is accessible through http://orca.cs.ucsb.edu/sat_portal) for solving Boolean satisfiability. The portal provides a simple and public interface to a sophisticated and complex Grid application—GridSAT—running on a large set of distributed computational resources hosted in different large-scale national computing centers (i.e. the national cyberinfrastructure circa 2005). In this paper we describe the design goals of the portal and how it has influenced some of the application features. We also describe how the adaptive and self-tuning features of Grid applications (written from first principles) make the portal simpler and easier to implement. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Resource co-allocation is one of the crucial problems affecting the utility of the grid. Because the numbers of the application tasks and amounts of required resources are enormous and quick responses to the requirements of users are necessary in the real grid environment, real-time resource co-allocation may be large-scale. A parallel resource co-allocation algorithm based on the framework for mapping with resource co-allocation is proposed in this paper. Through the result of experiments, it is concluded that the parallel method reduces the execution time of the resource co-allocation algorithm significantly, and makes the overall response time to the end-users small. [Copyright &y& Elsevier]

Distributed cryptographic computing system plays an important role since cryptographic computing is extremely computation sensitive. However, no general cryptographic computing system is available. Grid technology can give an efficient computational support for cryptographic applications. Therefore, a general-purpose grid-based distributed computing system called DCCS is put forward in this paper. The architecture of DCCS is simply described at first. The policy of task division adapted in DCCS is then presented. The method to manage subtask is further discussed in detail. Furthermore, the building and execution process of a computing job is revealed. Finally, the details of DCCS implementation under Globus Toolkit 4 are illustrated. [ABSTRACT FROM AUTHOR]

To dynamically improve system selections of the waiting jobs under de-centralized scheduling frameworks, each computation system together with its neighbors is assumed to compose a subgrid, and distributed backfilling is proposed as the scheduling strategy in each subgrid. Whenever a job terminates, distributed backfilling is triggered to re-backfill all waiting jobs on the corresponding subgrid in order of their submittal. Each subgrid is overlapped with another, so the waiting jobs may be migrated around the Grid. To evaluate distributed backfilling, Grid resources and scheduling framework are configured, the model of Grid workload is constructed by extending the workload models of parallel systems and the Job Scheduling Simulation System is designed to simulate the process of de-centralized job scheduling. In addition, job speedup is presented as performance metrics of scheduling strategies. Results show the dynamic optimization of system selections brought by distributed backfilling is Grid-wide, and can improve scheduling performance remarkably as long as Grid load is not too light and the job migration costs are not too high. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Computational Grids (CGs) are large scale dynamical networks of geographically distributed peer resource clusters. These clusters are independent but cooperating computing systems bound by a management framework for the provision of computing services, called Grid Services. In its basic form, the Grid scheduling problem consists in finding at least one cluster that has the capacity to handle, within the constraints of a specified quality of service, a user service request submitted to the CG. Since CGs span distinct management domains, the scheduling process has to be decentralized. Furthermore, it has to account for the ubiquitous uncertainty on the state of the CG. In this paper, we propose a scalable distributed Entropy-based scheduling approach that utilizes a Markov chain model to capture the dynamics of the service capacity state. An entropy-based quantification of the uncertainty on the service capacity information is developed and explicitly integrated within the proposed Grid scheduling approach. The performance of the proposed scheduling strategy is validated, through simulation, against a random delegation scheme and a load balancing-based scheduling strategy with respect to throughput, exploitation and convergence speed, respectively. [ABSTRACT FROM AUTHOR]

Cooperation of multi-domain massively parallel processor systems in computing grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of multisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, compared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present GridSAT – a distributed and high performance complete satisfiability solver – and its application to a set of complex and previously unsolved satisfiability problems. Based on the sequential Chaff [M. Moskewicz, C. Madigan, Y. Zhao, L. Zhang, S. Malik. Chaff: Engineering an Efficient SAT Solver. Proceedings of the 38th Design Automation Conference (DAC2001), Las Vegas, June 2001] algorithm, we combine new distributed clause “learning” techniques with an efficient and autonomous grid implementation both to speed the time to solution and to solve problems too complex for other general solvers. By automatically adapting to changes in the availability of machines and carefully distributing the clause database, we show how GridSAT has been able to use, continuously, a diverse and dynamically changing resource pool to solve previously unsolved problems from the SAT 2002 [SAT 2002 Competition. http://www.satlive.org/SATCompetition/] and the SAT 2003 [SAT 2003 Competition. http://satlive.org/SATCompetition/2003/] competitions. We describe our enhancements to the Chaff learning algorithm that have enabled an efficient distributed implementation, and detail the technological approach we have taken to realizing this implementation. [Copyright &y& Elsevier]

Abstract: This paper describes the development and application of a piece of engineering software that provides a problem solving environment (PSE) capable of launching, and interfacing with, computational jobs executing on remote resources on a computational grid. In particular it is demonstrated how a complex, serial, engineering optimisation code may be efficiently parallelised, grid-enabled and embedded within a PSE. The environment is highly flexible, allowing remote users from different sites to collaborate, and permitting computational tasks to be executed in parallel across multiple grid resources, each of which may be a parallel architecture. A full working prototype has been built and successfully applied to a computationally demanding engineering optimisation problem. This particular problem stems from elastohydrodynamic lubrication and involves optimising the computational model for a lubricant based on the match between simulation results and experimentally observed data. [Copyright &y& Elsevier]

We present the latest instantiation of GridSAT [1], a distributed and complete satisfiability solver that is explicitly designed to aggregate Grid resources for application performance. GridSAT was previously shown to outperform the state-of-the-art sequential solvers. In this work, we explore the unprecedented solving power GridSAT enables through algorithmic and implementation innovations. We describe the implementation techniques that allow GridSAT to leverage a variety of high-end batch-scheduled resources, clusters, interactive workstations, and personal computing resources through autonomous scheduling, checkpoint scheduling, and work migration. These innovations have allowed GridSAT to solve a set of ‘hard’ and previously unsolved industrial and community satisfiability problems. In addition to this new solution power, GridSAT also outperforms the otherwise highest performance general solvers on the annual SAT competition [21] performance benchmarks. [ABSTRACT FROM AUTHOR]

Fault detection and propagation in a computational grid requires a comprehensive framework that takes in consideration the various grid environmental conditions such as the asynchronous nature of communication and the uncertainty on the disseminated fault information. The paper presents a fault-tolerance framework that provides the necessary models to manage the local faulty behavior associated with the operation of hosted services. The framework includes a quantification mechanism of the fault vulnerability of grid nodes and their hosted services. The resulting measures of fault vulnerability are globally disseminated to enable the synthesis of decentralized fault-tolerant decision making strategies. [ABSTRACT FROM AUTHOR]

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We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various ways. [ABSTRACT FROM AUTHOR]

Computational grids are large scale distributed networks of peer clusters of computing resources bounded by a decentralized management framework for the purpose of providing computing services, called grid services. The scheduling problem consists in finding the clusters that host the required set of grid services with a sufficient available capacity to handle user service requests in compliance with some specified quality of service. The interplay of intermittent resource participation, resource load dynamics, network latency and processing delay, and random subsystem failures creates a ubiquitous uncertainty on the state of the grid capacity to handle user requests. In addition to the need to account for this uncertainty, the scheduling strategy has to be decentralized since computational grids span distinct management domains. In this paper, we propose a decentralized scheduling strategy that views the dynamics of the grid service capacity as a stochastic process modeled by a Markov chain. The proposed scheduling scheme uses this model to predict the future local availability of resources. This is consolidated by a confidence model that approximates the future ability of peer clusters to successfully handle delegated service requests. The scalability of the proposed scheduling strategy is illustrated through simulation. [ABSTRACT FROM AUTHOR]

Abstract: In this work we consider some of the computational aspects of viscous fluid flow through two-dimensional curvilinear domains. Associated with the use of the von Mises transformation in mapping a physical plane into a computational plane are some complexities that arise in the main part due to the infinite nature of the Jacobian of the transformation. Some recommendations are made in this work to overcome some of the arising computational difficulties. [Copyright &y& Elsevier]

A general scheduling framework (GSF) for independent tasks in computational Grid is proposed in this paper, which modeled by Petri net and located on the layer of Grid scheduler. Furthermore, a new mapping algorithm aimed at time and cost is designed on the basis of this framework. The algorithm uses weighted average fuzzy applicability to express the matching degree between available machines and independent tasks. Some existent heuristic algorithms are tested in GSF, and the results of simulation and comparison not only show good flexibility and adaptability of GSF, but also prove that, given a certain aim, the new algorithm can consider the factors of time and cost as a whole and its performance is higher than those mentioned algorithms. [ABSTRACT FROM AUTHOR]

The main goal of the Grid-TLSE project is to design an expert site that provides an easy access to a number of sparse matrix solver packages allowing their comparative analysis on user-submitted problems, as well as on matrices from collections also available on the site. The site provide user assistance in choosing the right solver for its problems and appropriate values for the solver parameters. A computational Grid is used to deal with all the runs arising from user requests. After an overview of the project, we discuss on some of the main difficulties we face in Grid-TLSE: considering the amount of softwares that will be available, facilitating to facilitate their deployment and their exploitation over a Grid is crucial. We make use of an abstract meta-data based description of solvers and have introduced the concept of scenario, i.e. a high-level user assistance description that provides ways of discovering automatically the most appropriate solvers based on their description. The scenarios are used for generating the dynamic workflows that will be executed over the Grid and take advantage of some of the features available in the DIET Grid middleware. We also report on the introduction of a semantic-based component model for efficient service trading. [ABSTRACT FROM AUTHOR]

Background: In health applications, and elsewhere, 3D data sets are increasingly accessed through the Internet. To reduce the transfer time while maintaining an unaltered 3D model, adequate compression and decompression techniques are needed. Recently, Grid technologies have been integrated with Web Services technologies to provide a framework for interoperable application-to-application interaction.Objectives: The paper describes an implementation of the Edgebreaker compression technique exploiting web services technology and presents a novel approach for using such services in a Grid Portal. The Grid portal, developed at the CACT/ISUFI of the University of Lecce, allows the processing and delivery of biomedical images (CT--computerized tomography--and MRI--magnetic resonance images) in a distributed environment, using the power and security of computational Grids.Methods: The Edgebreaker Compression Web Service has been deployed on a Grid portal and allows compressing and decompressing 3D data sets using the Globus toolkit GSI (Globus Security Infrastructure) protocol. Moreover, the classical algorithm has been modified extending the compression to files containing more than one object.Results and Conclusions: An implementation of the Edgebreaker compression technique and related experimental results are presented. A novel approach for using the compression web service in a Grid portal allowing storing and preprocessing of huge 3D data sets, and subsequent efficient transmission of results for remote visualization is also described. [ABSTRACT FROM AUTHOR]

A computational Grid system with the simple architecture was constructed based on Globus and the concept of web application for the quantum chemical calculation. The computational Grid provides interfaces to a web-based input module and several molecular orbital calculation packages. Some aspects of the cluster modeling of the Ge(001) surface were tested on the constructed Grid. A number of conditions and parameters of the cluster model can be easily varied on the Grid, enabling concurrent testing of multiple choices of the model possible. These models were benchmarked on the Grid system. After that, the potential energy surface of the acetylene molecule moving over the model Ge(001) surface was scanned, in an effort to understand the adsorption reaction. Each point of the potential energy surface was calculated on the distributed node of the Grid system. These results demonstrate that the concept of high throughput computing can be successfully adapted to computational chemistry with a computational Grid. The result of modeling for the Ge surface itself is also described and could be of some interest. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the interactions between agents representing users, services and resources to solve resource scheduling optimization in computational grid. In order to reduce the computational complexity, we further decompose the grid resource allocation optimization into subproblems: grid user agent-grid service agent in service market and grid service agent-grid resource agent in resource market. Two-level market converges to its optimal points; a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. It demonstrates a practical approach to market responsive resource pricing that can benefit grid providers and users alike. The paper presents two-level market grid resource pricing that is an iterative algorithm used to perform optimal resource allocation. The experiment shows that two-level market based resource pricing scheme outperforms one level market scheme in terms of task completion time and resource allocation efficiency. [Copyright &y& Elsevier]

In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e‐scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide‐spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP. [ABSTRACT FROM AUTHOR]

The National Research Grid Initiative (NAREGI) is one of the major Japanese national IT projects currently being conducted. NAREGI will cover the period 2003-2007, and collaboration among industry, academia, and the government will play a key role in its success. The Center for Grid Research and Development has been established as a center for R&D of high-performance, scalable grid middleware technologies, which are aimed at enabling major computing centers to host grids over high-speed networks to provide a future computational infrastructure for scientific and engineering research in the 21st century. As an example of utilizing such grid computing technologies, the Center for Application Research and Development is conducting research on leading-edge, grid-enabled nanoscience and nanotechnology simulation applications, which will lead to the discovery and development of new materials and next-generation nanodevices. These two centers are collaborating to establish daily research use of a multiteraflop grid testbed infrastructure, which will be built to demonstrate the advantages of grid technologies for future applications in all areas of science and engineering. [ABSTRACT FROM AUTHOR]

One benefit of a computational Grid is the ability to run high-performance applications over distributed resources simply and securely. We demonstrated this benefit with an experiment in which we studied the protein-folding process with the CHARMM molecular simulation package over a Grid managed by Legion, a Grid operating system. High-performance applications can take advantage of Grid resources if the Grid operating system provides both low-level functionality as well as high-level services. We describe the nature of services provided by Legion for high-performance applications. Our experiences indicate that human factors continue to play a crucial role in the configuration of Grid resources, underlying resources can be problematic, Grid services must tolerate underlying problems or inform the user, and high-level services must continue to evolve to meet user requirements. Our experiment not only helped a scientist perform an important study, but also showed the viability of an integrated approach such as Legion's for managing a Grid. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many distributed applications must meet stringent performance requirements even when the performance characteristics of the underlying systems and networks vary significantly at runtime. Runtime adaptation can be used to tolerate such changes, but sophisticated adaptive distributed programs can be extremely challenging to design, implement and debug. This paper proposes a language called Program Control Language (PCL) that provides a novel means of specifying adaptations in distributed applications. PCL is based on an abstract, global representation of a distributed program (a static task graph), which enables a programmer to reason about and to describe a wide range of application-specific adaptation strategies at a high level, using a few key mechanisms. PCL provides simple high-level syntax for local and remote adaptation operations, local and remote performance monitoring (and aggregation), and for performing adaptations synchronously or asynchronously with respect to the execution of the application process initiating the adaptation. The global task graph representation enables remote performance metrics and adaptation operations to be specified in simple global terms by any process, and the compiler and runtime system automatically perform the communication and synchronization required for the remote operations. The paper describes the conceptual adaptation framework, the PCL language, and our implementation of the PCL compiler and runtime system. The paper uses three adaptative applications examples to illustrate the capabilities and benefits of PCL, and to show experimentally that the performance overheads of using PCL for implementing an adaptive application are negligible. [Copyright &y& Elsevier]

Spatial interpolation is widely used in geographical information systems to create continuous surfaces from discrete data points. The creation of such surfaces, however, can involve considerable computation, especially when large problems are addressed, because of the need to search for neighbors on which to base interpolation calculations. Computational Grids provide the computing resources to tackle spatial interpolation in a timely way. The objective of this paper is to investigate the use of domain decomposition for a distributed inverse-distance-weighted spatial interpolation algorithm; the algorithm runs using the Globus Toolkit (GT) in a heterogeneous Grid computing environment. The interpolation algorithm is modified for implementation in the Grid by using a quadtree to spatially index and adaptively decompose the interpolation problem to balance processing loads. In addition, the GT allows the distributed algorithm to couple multiple machines, potentially of different architectures, to dynamically schedule the decomposed sub-problems through Globus services and protocols (e.g., resource management, data transfer). Experiments are conducted to test how well this distributed IDW interpolation algorithm scales to heterogeneous grid computing environments using irregularly distributed geographical data sets. [Copyright &y& Elsevier]

This paper presents an Agent-based Computational Grid (ACG), which applies the concept of CORBA and agent to computational grid. The ACG system is used to implement a uniform higher level management of the computing resources and services on the Grid, and provide users with a consistent and transparent interface for accessing such services. In ACG grid, grid services are implemented by CORBA or by grid agent. Grid agents and CORBA objects will interact with each other to achieve user''s service request. Our solution is the creation of a bridge between the CORBA and grid agents. The solution provides with the opportunity of considering an agent as a CORBA service and accessing CORBA services even from a grid agent. Thus, in AGC grid, existing legacy systems can be easily exploited as grid services. In this paper, firstly, the features of ACG grid are described, and then the design and implementation are given. Finally, some conclusions are given. [Copyright &y& Elsevier]

We describe how the Grid enables new research possibilities in astronomy through multi-wavelength images. To see sky images in the same pixel space, they must be projected to that space, a computer-intensive process. There is thus a virtual data space induced that is defined by an image and the applied projection. This virtual data can be created and replicated with Planners and Replica catalog technology developed under the GriPhyN project. We plan to deploy our system (MONTAGE) on the U.S. Teragrid. Grid computing is also needed for ingesting data—computing background correction on each image—which forms a separate virtual data space. Multi-wavelength images can be used for pushing source detection and statistics by an order of magnitude from current techniques; for optimization of multi-wavelength image registration for detection and characterization of extended sources; and for detection of new classes of essentially multi-wavelength astronomical phenomena. The paper discusses both the Grid architecture and the scientific goals. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The NorduGrid is the pioneering Grid project in Scandinavia. The purpose of the project is to create a Grid computing infrastructure in Nordic countries. The cornerstone of the infrastructure adopted at NorduGrid is the Globus toolkit developed at Argonne National Laboratory and University of Southern California. It is, however, missing several important high-level services. With the need to provide working production system, NorduGrid has developed its own solution for the most essential parts. An early prototype implementation of the proposed architecture is being tested and further developed. Aiming at simple but yet functional system capable of handling common computational problems encountered in the Nordic scientific communities, we were choosing simple, but still functional solutions with the necessary parts implemented first. [Copyright &y& Elsevier]

Developing advanced applications for the emerging national-scale ‘Computational Grid’ infrastructures is still a difficult task. Although Grid services are available that assist the application developers in authentication, remote access to computers, resource management, and infrastructure discovery, they provide a challenge because these services may not be compatible with the commodity distributed-computing technologies and frameworks used previously. The Commodity Grid project is working to overcome this difficulty by creating what we call Commodity Grid Toolkits (CoG Kits) that define mappings and interfaces between Grid and particular commodity frameworks. In this paper, we explain why CoG Kits are important, describe the design and implementation of a Java CoG Kit, and use examples to illustrate how CoG Kits can enable new approaches to application development based on the integrated use of commodity and Grid technologies. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We are on the path to meet the major challenges ahead for TCAD. The emerging computational grid will ultimately solve the challenge of limited computational power. The Modular TCAD Framework will solve the TCAD software challenge once more developers agree to cooperate. This framework also provides the ideal platform for rapid implementation of the many models needed in a partial differential equation solver [ABSTRACT FROM PUBLISHER]

In computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence fault tolerance plays a key role in grid to avoid the problem of unreliability. The two main techniques for implementing fault tolerance in grid environment are check pointing and replication. This paper proposes a real time approach to a replication technique named as FMFRS (Fault Tolerant most fitting resource scheduling algorithm) to improve the fault tolerance of the fittest resource scheduling algorithm. The proposed method is to improve the fault tolerance by using fittest resource scheduling algorithm, by scheduling the job in coordination with job replication when the resource has low reliability and checking the parameters like Fault Tolerance capacity and Node's Reliability. Based on the reliability index of the resource, the resource is identified as critical. [ABSTRACT FROM AUTHOR]

• Rigorous approach for the a priori design of computational grids. • Identification of characteristic scales based on 2-D Fourier analysis. • "A-priori" design of the computational grid starting from the characteristic scales. • Significant decrease of run times and negligible errors on outflow hydrographs. The increasing availability of high-resolution terrain data fostered the development of two-dimensional hydrodynamic-based surface runoff models at basin scale, for flood hazard assessment and mapping purposes. The generation of the computational domain is one of the most important preliminary steps for practical applications of this kind of modeling. Several studies can be found in the technical literature showing the influence exerted by the computational grid type used on the results in terms of water depth, flow velocity and discharge. Several approaches were proposed for the mesh generation even though, in practice, it is subject to the modeller choice and experience. In any case, the reason why a specific mesh is used is rarely discussed and its impact on the model results in terms of outflow hydrographs, runoff volumes and flooded areas extent is seldom analysed. In this work a rigorous approach for an a priori design of a computational grid is proposed. The a priori term suggests that the proposed procedure aims at generating a computational mesh, close to the optimal one, in a time-saving process which is not based on trial meshes and trial simulations. Namely, the process is based on the application of the two-dimensional Fourier analysis to a high-resolution digital elevation model in order to compute the power spectrum and finally to identify the significant waves lengths and the characteristic scales. The latter ones are located in the catchment and used for the generation of a computational grid characterized by different elements size. The simulations of surface runoff at a basin scale have shown that the application of the proposed method for the a priori construction of the grid leads to the generation of a computational mesh which reduces the calculation time by approximately 87%, if compared to a fine grid taken as reference, with negligible errors on outflow hydrographs and flooded areas extent. [ABSTRACT FROM AUTHOR]

HEP collaborations are deploying grid technologies to address petabyte-scale data processing challenges. In addition to file-based event data, HEP data processing requires access to terabytes of non-event data (detector conditions, calibrations, etc.) stored in relational databases. Inadequate for non-event data delivery in these amounts, database access control technologies for grid computing are limited to encrypted message transfers. To overcome these database access limitations one must go beyond the existing grid infrastructure. A proposed hyperinfrastructure of distributed database services implements efficient secure data access methods. We introduce several technologies laying a foundation of a new hyperinfrastructure. We present efficient secure data transfer methods and secure grid query engine technologies federating heterogeneous databases. Lessons learned in a production environment of ATLAS Data Challenges are presented. [ABSTRACT FROM AUTHOR]