Data Grid

Sofiare agents are expected to play an increasingly important role in computational, service, and data grids built around the Internet. In this paper we discuss attributes of mobile agent andpropose criteria that may be used for qualitative and quantitative evaluation of mobile agent systems.

We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants to the "prior knowledge" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants to the "agnostic learning" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performances. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: http://www.agnostic.inf.ethz.ch/ .

Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever before. To solve these complicated problems, grid computing becomes a popular tool. A grid environment collects, integrates, and uses heterogeneous or homogeneous resources scattered around the globe by a high-speed network. A grid environment can be classified into two types: computing grids and data grids. This paper mainly focuses on computing grids.In computing grid, job scheduling is a very important task. A good scheduling algorithm can assign jobs to resources efficiently and can balance the system load.In this paper, we propose a hierarchical framework and a job scheduling algorithm called Hierarchical Load Balanced Algorithm (HLBA) for Grid environment. In our algorithm, we use the system load as a parameter in determining a balance threshold. And the scheduler adapts the balance threshold dynamically when the system load changes. The main contributions of this paper are twofold. First, the scheduling algorithm balances the system load with an adaptive threshold and second, it minimizes the makespan of jobs. Experimental results show that the performance of HLBA is better than those of other algorithms.► A hierarchical framework and a job scheduling algorithm for grid are proposed. ► The algorithm is called the Hierarchical Load Balanced Algorithm (HLBA). ► The main contributions are system load balancing and makespan minimization.

Nowadays many data grid applications need to manage and process a huge amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage the publication of scientific data in a more open manner than is currently the case, and many e-Science projects have an urgent need to interconnect legacy and independently operated databases through a set of data access and integration services. In the data grid area a set of dynamic and adaptive services could address specific issues related to automatic data management aiming at both providing high performance and fully exploiting a grid infrastructure. In this paper we introduce the Grid-DBMS concept, a framework for dynamic data management in grid environments, highlighting its requirements, architecture, components and services.

In order to handle spatial data efficiently, as required in computer aided design and geo-data applications, a database system needs an mdex mechanism that ti help it retrieve data items quickly accordmg to their spatial locations However, traditional mdexmg methods are not well suited to data oblects of non-zero size located m multidimensional spaces In this paper we describe a dynarmc mdex structure called an R-tree winch meets this need, and give algorithms for searching and updatmg it. We present the results of a series of tests which indicate that the structure performs well, and conclude that it is useful for current database systems m spatial applications

Grid computing is a type of distributed computing system that provides access to various computational resources which are shared by different organizations, in order to create an integrated powerful virtual computer. Nowadays, grid is known as an essential technology which is used for different kinds of high performance applications and it is believed that it will be applied more and more in the future as technology progresses. Data replication is a common method used in distributed environments to improve ease of data access and to provide a high level of data availability, increased fault tolerance and data reliability; and that's why this method is used for data management in data grid systems. Since the data files are very large and the Grid storages are limited, managing replicas in storage for the purpose of more effective utilization requires more attention. In this paper, a novel data replication strategy, called Replication with Dynamic Threshold (RDT) is proposed that uses a new threshold for characterizing the number of appropriate sites for replication. Appropriate sites have the higher number of access for that particular replica from other sites. It also minimizes access latency by selecting the best replica when various sites hold replicas. The simulated results with OptorSim, i.e. European Data Grid simulator show that the RDT strategy gives better performance compared to the other algorithms and prevents the unnecessary creation of replicas which leads to efficient storage usage.

ABSTRACT The emergence of low-cost PC clusters together with the standardization of programming models (MPI and OpenMP) have paved the way for parallel computing to come into production use. In all domains of high performance computing, parallel execution routinely is considered as one of the major sources of performance. In some domains, like computational .uid dynamics, commercial codes already o.er parallel execution as an option. In new domains, like bioinformatics, parallel execution is considered early in the design of algorithms and software. Besides clusters, grid computing is receiving increasing attention.

The GridNets conference series is an annual international meeting which provides a focused and highly interactive forum where researchers and technologists have the opportunity to present and discuss leading research, developments, and future directions in the grid networking area. The objective of this event is to serve as both the premier conference presenting the best grid networking research and a forum where new concepts can be introduced and explored.

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Grid Portals, based on standard web technologies, are emerging as important and useful user interfaces to computational and data Grids. Grid Portals enable Virtual Organizations, comprised of distributed researchers to collaborate and access resources more efficiently and seamlessly. The Astrophysics Simulation Collaboratory (ASC) Grid Portal provides a framework to enable researchers in the field of numerical relativity to study astrophysical phenomenon by making use of the Cactus computational toolkit. We examine user requirements and describe the design and implementation of the ASC Grid Portal.

The sedimentary architecture of active margin basins, including back-arc basins, is known only from a few end-members that barely illustrate the natural diversity of such basins. Documenting more of these basins types is the key to refining our understanding of the tectonic evolution of continental margins. This paper documents the sedimentary architecture of an incipient back-arc basin 200 km behind the active Hikurangi subduction margin, North Island, New Zealand.

The spatial distribution of stocks of water is useful in studying flood, water pollution and water supply problems. Flood prone and closed basin watersheds benefit from spatial water balance studies in understanding the hydrologic processes and deal with excess water problems. In this study, we present results of a study of the hydrology Devils Lake basin of the Red River of the North, northeastern North Dakota partitioned in to two parts. Part 1 addresses the hydrometeorological analysis and lake surface area mapping of the basin and Part 2 deals with spatial surface water balance modeling using Landsat images and geographic information system (GIS). Hydrometeorological analysis using 100-years of historical record for the Devils Lake basin was conducted to capture the historical variability of the flood. In addition, surface area of the lake was mapped using Landsat image from 1991 to 2003. The Hydrometeorological analysis of the historical data showed the runoff inflow from upstream watersheds driven by snowmelt and spring rain falling on wet soil is the dominant source of the lake rise. Results show an increase in lake surface area by 117% between 1993 and 2003. The analysis also showed a correlation and possible interactions between the lake and the Spritwood aquifer indicating potential contribution of the groundwater flux to the water budget.

In data grids, many distributed scientific and engineering applications often require access to a large amount of data (terabytes or petabytes). Data access time depends on bandwidth, especially in a cluster grid. Network bandwidth within the same cluster is larger than across clusters. In a communication environment, the major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks (WANs) and Internet. Effective scheduling in such network architecture can reduce the amount of data transferred across the Internet by dispatching a job to where the needed data are present. Another solution is to use a data replication mechanism to generate multiple copies of the existing data to reduce access opportunities from a remote site. To utilize the above two concepts, in this paper we develop a job scheduling policy, called HCS (Hierarchical Cluster Scheduling), and a dynamic data replication strategy, called HRS (Hierarchical Replication Strategy), to improve the data access efficiencies in a cluster grid. We simulate our algorithm to evaluate various combinations of data access patterns. We also implement HCS and HRS in the Taiwan Unigrid environment. The simulation and experiment results show that HCS and HRS successfully reduces data access time and the amount of inter-cluster-communications in comparison with other strategies in a cluster grid.

Data Grids are becoming increasingly important in scientific communities for sharing large data collections and for archiving and disseminating them in a digital library framework. The Storage Resource Broker provides transparent virtualized middleware for sharing data across distributed, heterogeneous data resources separated by different administrative and security domains. The MySRB is a web-based interface to the SRB that provides a user-friendly interface to distributed collections brokered by the SRB. In this paper we briefly describe the use of the SRB infrastructure as tools in the Data Grid Architecture for building distributed data collections, digital libraries, and persistent archives. We also provide details about the mySRB and its functionalities.

Timely worldwide distribution of biosequence and bioinformatics data depends on high performance networking and advances in Internet transport methods. The Bio-Mirror project focuses on providing up-to-date distribution of this rapidly growing and changing data. It offers FTP, Web and Rsync access to many high-volume databanks from several sites around the world. Experiments with data grids and other methods offer future improvements in biology data distribution.

Grid fechnologies are more and more used in scienfi c as well as in industrial envimnments but oren documentation and the correct usage are either not suf cient or not too well understood. Comprehensive training with handson experience helps people rsf to understand the technulogy and second to use if in a correct and ef cient way. We have organised and run several training sessions in different locations all over the worldandprovide our experience. The major factors of success are a solid base of theoretical lectures and, more dominunfly, a facility that allows for practical Grid exercises during and possibly after tutorial sessions. 1. Introduction to Grid Computing & EU DataGrid Project: An overview is given of Grid computing in general and the EDG project. The project organisation and the general software architecture are described, and some related projects are outlined. Key concepts: Grid Computing, Data Grids, intemational Grid projects worldwide , EU DataGrid project 2. Security Issues: Secure access to Grid resources is a major issue and security is one of the first things a user has to deal with when starting to use the Grid. A basic overview about current security solutions in the EDG project is given. Key concepts: GSI Security (Grid Securiry Infiastniclure provided by the Globus Alliance), user and host certificates, Virtual Organisation Management 3. Testbed Overview: EDG deploys a large-scale testbed that spans several sites all over Europe. Definitions are given of the available Grid services and resources and a detailed overview of the testbed is provided.

This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and discusses various representative economy-based systems, both historical and emerging, for cooperative and competitive trading of resources such as CPU cycles, storage, and network bandwidth. It presents an extensible, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies. It also presents commodity and auction models for resource allocation. The use of commodity economy model for resource management and application scheduling in both computational and data grids is also presented.

Because of the dynamic and heterogeneous nature of a grid infrastructure, the client/server paradigm is a common programming model for these environments, where the client submits requests to several geographically remote servers for executing already deployed applications on its own data. According to this model, the applications are usually decomposed into independent tasks that are solved concurrently by the servers (the so called Data Grid applications). On the other hand, as many scientific applications are characterized by very large set of input data and dependencies among subproblems, avoiding unnecessary synchronizations and data transfer is a difficult task. This work addresses the problem of implementing a strategy for an efficient task scheduling and data management in case of data dependencies among subproblems in the same Linear Algebra application. For the purpose of the experiments, the NetSolve distributed computing environment has been used and some minor changes h...

Data privacy is a major threat to the widespread deployment of data grids in domains such as health care and finance. We propose a novel technique for obtaining knowledge-by way of a data mining model-from a data grid, while ensuring that the privacy is cryptographically secure. To the best of our knowledge, all previous approaches for solving this problem fail in the presence of malicious participants. In this paper we present an algorithm which, in addition to being secure against malicious members, is asynchronous, involves no global communication patterns, and dynamically adjusts to new data or newly added resources. As far as we know, this is the first privacy-preserving data mining algorithm to possess these features in the presence of malicious participants. Simulations of thousands of resources prove that our algorithm quickly converges to the correct result. The simulations also prove that the effect of the privacy parameter on the convergence time is logarithmic. * We thank Intel Corporation and the Mafat Institute for Research and Development for their generous support of this research.

The paper describes the overall design and implementation of the geographically distributed computing and data storage centre as built by the Nordic DataGrid Facility (NDGF) with the driving goal to serve the needs of the Large Hadron Collider research at CERN. In striving to federate heterogeneous resources in different administrative domains, NDGF chose to create an infrastructure using software services offered by different providers: ARC for computing, dCache for storage, SGAS for usage accounting, and elements of gLite for interoperability with the worldwide LHC Computing Grid.

With the regular progress of technology and infrastructures, a growing number of grid applications are developed and deployed for life science and medical research. At the last HealthGrid conference in April 2005 in Oxford, many groups described successful usage of grids for compute intensive calculations. Very large scale deployment of a biomedical application in the area of drug discovery has been achieved on EGEE during 2005. On the other hand, beside a few pioneers, very few data grids have been deployed so far and knowledge grids are still at a conceptual level. This situation is expected to evolve quickly as many projects are focussed on developing data management services and knowledge management tools relevant to biomedical sciences. At this stage, it is important to identify the potential bottlenecks and to define a roadmap for the wide adoption of grids for healthcare. This article presents an analysis of the present adoption of grids for biomedical sciences and healthcare...

In an increasing number of scientific disciplines, large data collections are emerging as important community resources. In this paper, we introduce design principles for a data management architecture called the data grid. We describe two basic services that we believe are fundamental to the design of a data grid, namely, storage systems and metadata management. Next, we explain how these services can be used to develop higher-level services for replica management and replica selection. We conclude by describing our initial implementation of data grid functionality.

With the advent of the data grid came a novel distributed scientific computing paradigm known as service-oriented science. Among the plethora of systems included under this framework are scientific workflow management systems, which enable large-scale process scheduling and execution. To ensure quality of service, these systems typically seek to minimize workflow execution time as well as costs for slices of data grid access. The geospatial domain, among other sciences, involves yet another optimization factor, the accuracy of results. The relationship between execution time and workflow accuracy can often be exploited to offer more flexibility in handling user preferences. We present a system which meets user constraints through a dynamic adjustment of the accuracy of workflow results.

We designed a semantic enabled metadata framework using ontology for multi-disciplinary and multi-institutional large-scale scientific data sets in a Data Grid setting. Two main issues are addressed: data integration for semantically and physically heterogeneous distributed knowledge stores, and semantic reasoning for data verification and inference in such a setting. This framework enables data interoperability between otherwise semantically incompatible data sources, cross-domain query capabilities and multi-source knowledge extraction. In this paper, we present the basic system architecture for this framework, as well as an initial implementation. We also analyse a real-life scenario and show integration of our framework into the PetaShare Data Grid where multi-disciplinary data archives are geographically distributed across six research institutions in Louisiana. he has been involved in the PetaShare project and his research mainly focuses on ontology metadata in grid and distributed computing environments.

The focus of this paper is on the design and implementation of a critical program in structural biology onto two computational and data grids. The first is the Buffalo-based ACDC grid, which uses facilities at several research institutions in Western New York. The second is Grid2003, the iVDGL grid established late in 2003 primarily for physics and astronomy applications. In this paper, we present an overview of the ACDC Grid and Grid2003, focusing on the implementation of several new ACDC computational and grid tools.

The traditional task of managing and monitoring a network has never been a trivial one. With recent changes in computing and networking, the area of distributed systems management faces new challenges and increasing complexity. Research in the relevant field reveals that, while there are many research and commercial solutions available, some of them are based on proprietary standards. Others focus on monitoring, while lacking the ability to actively make modifications and finetuning. Some others have a narrow target group. This paper proposes a framework for the management of distributed applications. The managed hosts are treated as integral parts of the deployment and not as stand alone, isolated entities. The framework is based on SNMP and is not limited to monitoring. On the contrary, it is capable of carrying out SNMP-SET commands, actively modifying the run-time parameters of the managed application. Finally, it can perform the management of a variety of distributed systems, ranging from small clusters to larger scale deployments such as computational or data grids.

In wide area computing systems, it is often desirable to create remote read-only copies (replicas) of files. Replication can be used to reduce access latency, improve data locality, and/or increase robustness, scalability and performance for distributed applications. We define a replica location service (RLS) as a system that maintains and provides access to information about the physical locations of copies. An RLS typically functions as one component of a data grid architecture. This paper makes the following contributions. First, we characterize RLS requirements. Next, we describe a parameterized architectural framework, which we name Giggle (for GIGa-scale Global Location Engine), within which a wide range of RLSs can be defined. We define several concrete instantiations of this framework with different performance characteristics. Finally, we present initial performance results for an RLS prototype, demonstrating that RLS systems can be constructed that meet performance goals.

e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres is limited.

We review several aspects of building real-time streaming data Grid applications. Building on general purpose messaging system software (NaradaBrokering) and generalized collaboration services (GlobalMMCS), we are developing a diverse set of interoperable capabilities. These include dynamic information systems for managing short-lived collaborative service collections (“gaggles”), stream filters to support the integration of Geographical Information Systems services with data analysis applications, streaming ...

Data Grid environment seek to harness geographically distributed resources that deal with data-intensive problems such as those encountered in high energy physics, bio-informatics, and other disciplines. In general, grids enable the efficient sharing and management of computing resources for the purpose of performing large complex tasks. To be able to sharing data, it is recommended to use the replication technique. This technique provides an improvement in performance, fault tolerance and load balancing. The replication management and its implementation are not simple tasks and produce other problems, like consistency management of replicas. One of the concerns major in the consistency management approaches called optimistic, it is the conflicts resolution among replicas. In this paper we present negotiation mechanisms based on the various negotiation forms between virtual consistency agents to be able to critical situation for sites and to reduce the number of conflicts among repl...

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. To increase resource availability and to ease resource sharing in such environment, there is a need for replication services. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites. Replica placement mechanism is the process of identifying where to place copies of replicated data files in a Grid system. Choosing the best location is not an easy task. Current works find the best location based on number of requests and read cost of a certain file. As a result, a large bandwidth is consumed and increases the computational time. Authors proposed a GA-Based Replica Placement Mechanism (DBRPM) that finds the best locations to store replicas based on five criteria, namely, 1) Read Cost, 2) Storage Cost, 3) Sites' Workload, and 4) Replication Site.

Secret sharing and erasure coding-based approaches have been used in distributed storage systems to ensure the confidentiality, integrity, and availability of critical information. To achieve performance goals in data accesses, these data fragmentation approaches can be combined with dynamic replication. In this paper, we consider data partitioning (both secret sharing and erasure coding) and dynamic replication in data grids, in which security and data access performance are critical issues. More specifically, we investigate the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and/or replicated. The grid topology we consider consists of two layers. In the upper layer, multiple clusters form a network topology that can be represented by a general graph. The topology within each cluster is represented by a tree graph. We decompose the share replica allocation problem into two subproblems: the Optimal Intercluster Resident Set Problem (OIRSP) that determines which clusters need share replicas and the Optimal Intracluster Share Allocation Problem (OISAP) that determines the number of share replicas needed in a cluster and their placements. We develop two heuristic algorithms for the two subproblems. Experimental studies show that the heuristic algorithms achieve good performance in reducing communication cost and are close to optimal solutions.

The grid has emerged as a novel paradigm that supports seamless cooperation of distributed, heterogeneous computing resources in addressing highly complex computing and data management tasks. A number of software technologies have emerged to enable "grid computing". However, their exact nature, underlying principles, requirements, and architecture are still not fully understood and remain under-specified. In this paper, we present the results of a study whose goal was to try to identify the key underlying requirements and shared architectural traits of grid technologies. We then used these requirements and architecture in assessing five existing, representative grid technologies. Our studies show a fair amount of deviation by the individual technologies from the widely cited baseline grid architecture. Our studies also suggest a core set of critical requirements that must be satisfied by grid technologies, and highlight a key distinction between "computational" and "data" grids in terms of the identified requirements.

Deep Space Mission Systems (DSMS) lack the capability to provide end to and tracing of rdiission data products. These data products are simple products such as telemetry data, processing history, arld uplink datz. Additionally, there is a need to create Complex products (packages) which can contain onz or more of the simple products, along with descriptive information about the package itseif. The ability to track both types of these data products is crucial for providing visibility of in-process status along the data product pipeline between space and ground. Other applications of this informatiori include measuring pedomance of the space-to-ground data product pipeline and the timeliness of operational activities, which are key metrics in mission-critical, large-scale embedded systems that exist in muiri-node space-toground missions. In order to facilitate this tracing, information about the product format and the observation (mefadafa) can be defined, captured, and caMogec! per data product in the nipsline.

Genomic predictions of estimated breeding values (EBV) for dairy cattle include effects of tens of thousands of markers distributed over 30 chromosomes for many traits. There are so many numbers that data are difficult to compare, levels of detail are obscured, and data cannot easily be tabulated. Well-designed graphics can present more information in a smaller area than text or tables and provide insight into the data. Subtle differences can be detected more easily among graphics than among data grids, allowing information to be presented with greater density. Genomic data can be visualized at several levels, such as the distribution of marker effects across the genome and relationships among markers on the same chromosome. All markers affecting a trait can be plotted on the same ordinate to visualize the distribution of marker effects across the genome, colors or textures can be used to differentiate between chromosomes, and stacked graphs can be constructed to compare interesting groups of traits. Chromosomal EBV can be presented as high-resolution graphics embedded in text to provide an overview of individual animals for comparison to potential mates. Small multiples of chromosomal genetic correlation matrices from which nonsignificant values have been excluded can be used to identify interesting patterns of association among traits, such as that on chromosome 18 associated with calving traits, conformation, and economic merit. Line plots of marker effects for recessive traits can be used to quickly locate chromosomal regions in which causative mutations are probably located, identifying areas of interest for further study. These graphics are easily produced automatically and added to online query systems, providing users with novel information at little cost.

The functionality that is provided by Mass Storage Systems can be implemented using data grid technology. Data grids already provide many of the required features, including a logical name space and a storage repository abstraction. We demonstrate how management of tape resources can be integrated into data grids. The resulting infrastructure has the ability to manage archival storage of digital entities on tape or other media, while maintaining copies on distributed, remote disk caches that can be accessed through advanced discovery mechanisms. Data grids provide additional levels of data management including the ability to aggregate data into containers before storage on tape, and the ability to migrate collections across a hierarchy of storage devices.

This paper presents a high level architectural specification of MedioGRID, a research project aiming at implementing a real-time satellite image processing system for extracting relevant environmental and meteorological parameters on a Grid system. The presentation focuses on the key architectural decisions of the GRIDaware satellite image processing system, highlighting the technologies for each of the major components. An essential part of managing a global Data Grid is a monitoring system that is able to monitor and track all the site facilities, networks, and tasks in progress, all in real time. Considering this issue the paper analyzes the possible grid monitoring approaches, proposes a solution and presents a set of monitoring results for the MedioGRID data management subsystem.

We demonstrate the potential of using least-squares regression, generalized additive model, tree-based model, and neural network model on layers of environmental data grids for mapping site index in a case study. Grids of numerical environmental variables represented layered data, and a sparse site index plot network was located in the grids. Site index data were based on stem analysis (observed height at the index age of 50 years) of 431 lodgepole pine trees in 88 sample plots. The plots were established in a 17,460 km 2 boreal mixedwood forest of Alberta, Canada dominated by mature and over-mature stands. The generalized additive model presented a better fit and better adaptability to extreme data (i.e., mature stands) than the least squares nonlinear and other nonparametric techniques, such as the tree-based model and neural network model. Among the four models tested, nonlinear regression is of the data modeling culture, which assumes a stochastic data to relate productivity to environmental variables, and such models are optimized for estimation. Other three models belong to the algorithm modeling culture, which treat the relationship between productivity and independent variables as an unknown black box and try to find a function between them; therefore, these models are more suitable for prediction purpose. Implications for biophysical site index modelling with extreme data are discussed. Crown

Grid Portals, based on standard web technologies, are emerging as important and useful user interfaces to computational and data Grids. Grid Portals enable Virtual Organizations, comprised of distributed researchers to collaborate and access resources more efficiently and seamlessly. The Astrophysics Simulation Collaboratory (ASC) Grid Portal provides a framework to enable researchers in the field of numerical relativity to study astrophysical phenomenon by making use of the Cactus computational toolkit. We examine user requirements and describe the design and implementation of the ASC Grid Portal.

A preservation environment manages communication from the past while communicating with the future. Information generated in the past is sent into the future by the current preservation environment. The proof that the preservation environment preserves authenticity and integrity while performing the communication constitutes a theory of digital preservation. We examine the representation information that is needed about the preservation environment for a theory of digital preservation. The representation information includes descriptions of the preservation management policies, the preservation processes, and the state information that is needed to verify the correct working behavior of the system. We demonstrate rule-based data grids that can verify that prior policies correctly enforced preservation properties, while sending into the future descriptions of the current preservation management policies.