Scott Oster

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid

MOTIVATION The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.

TeraGrid Science Gateways and Their Impact on Science

Funded by the National Science Foundation (NSF), TeraGrid is one of the worlds largest distributed cyberinfrastructures for open scientific research. The project began in 2001 as the Distributed Tera-scale Facility, which linked computers, visualization systems, and data at four sites through a dedicated 40-gigabit optical network. Today TeraGrid includes 25 platforms at 11 sites and provides access to more than a petaflop of computing power and petabytes of storage. TeraGrid has three primary focus areas. Its deep goal is to support the most challenging computational science activities those that cannot be achieved without TeraGrid facilities. TeraGrids wide mission is to broaden its user base. The projects open goal is to achieve compatibility with peer grids and information services that allow development of programmatic interfaces to TeraGrid. The Science Gateways program seeks to provide researchers with easy access to TeraGrids high-performance computing resources. A look at four successful gateways illustrates the programs goals, challenges, and opportunities.

caGrid 1.0: an enterprise Grid infrastructure for biomedical research.

OBJECTIVE To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research. DESIGN An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG) program. It is designed to support a wide range of use cases in basic, translational, and clinical research, including 1) discovery, 2) integrated and large-scale data analysis, and 3) coordinated study. MEASUREMENTS The caGrid is built as a Grid software infrastructure and leverages Grid computing technologies and the Web Services Resource Framework standards. It provides a set of core services, toolkits for the development and deployment of new community provided services, and application programming interfaces for building client applications. RESULTS The caGrid 1.0 was released to the caBIG community in December 2006. It is built on open source components and caGrid source code is publicly and freely available under a liberal open source license. The core software, associated tools, and documentation can be downloaded from the following URL: https://cabig.nci.nih.gov/workspaces/Architecture/caGrid. CONCLUSIONS While caGrid 1.0 is designed to address use cases in cancer research, the requirements associated with discovery, analysis and integration of large scale data, and coordinated studies are common in other biomedical fields. In this respect, caGrid 1.0 is the realization of a framework that can benefit the entire biomedical community.

Introduce: An Open Source Toolkit for Rapid Development of Strongly Typed Grid Services

Service-oriented architectures and applications have gained wide acceptance in the Grid computing community. A number of tools and middleware systems have been developed to support application development using Grid Services architectures. Most of these efforts, however, have focused on low-level support for management and execution of Grid services, management of Grid-enabled resources, and deployment and execution of applications that make use of Grid services. Simple-to-use service development tools, which would allow a Grid service developer to leverage Grid technologies without needing to know low-level details, are becoming increasingly important for wider application of the Grid. In this paper, we describe an open-source, extensible toolkit, called Introduce, that supports easy development and deployment of Web Services Resource Framework (WSRF) compliant services. Introduce is designed to reduce the service development and deployment effort by hiding low level details of the Globus Toolkit and to enable the implementation of strongly typed services. In strongly typed services, a service produces and consumes data types that are well-defined and published in the Grid. This enables data-level syntactic interoperability so that clients and services can access and consume data elements programmatically and correctly. We expect that enabling strongly typed Grid services while lowering the difficulty of entry to the Grid via toolkits like Introduce will have a major impact to the success of the Grid and its wider adoption as a viable technology of choice in the commercial sector as well as in academic, medical, and government research.

A Grid-Based Image Archival and Analysis System

Here the authors present a Grid-aware middleware system, called GridPACS, that enables management and analysis of images in a massive scale, leveraging distributed software components coupled with interconnected computation and storage platforms. The need for this infrastructure is driven by the increasing biomedical role played by complex datasets obtained through a variety of imaging modalities. The GridPACS architecture is designed to support a wide range of biomedical applications encountered in basic and clinical research, which make use of large collections of images. Imaging data yield a wealth of metabolic and anatomic information from macroscopic (e.g., radiology) to microscopic (e.g., digitized slides) scale. Whereas this information can significantly improve understanding of disease pathophysiology as well as the noninvasive diagnosis of disease in patients, the need to process, analyze, and store large amounts of image data presents a great challenge.

Sharing data and analytical resources securely in a biomedical research Grid environment.

OBJECTIVES To develop a security infrastructure to support controlled and secure access to data and analytical resources in a biomedical research Grid environment, while facilitating resource sharing among collaborators. DESIGN A Grid security infrastructure, called Grid Authentication and Authorization with Reliably Distributed Services (GAARDS), is developed as a key architecture component of the NCI-funded cancer Biomedical Informatics Grid (caBIG). The GAARDS is designed to support in a distributed environment 1) efficient provisioning and federation of user identities and credentials; 2) group-based access control support with which resource providers can enforce policies based on community accepted groups and local groups; and 3) management of a trust fabric so that policies can be enforced based on required levels of assurance. MEASUREMENTS GAARDS is implemented as a suite of Grid services and administrative tools. It provides three core services: Dorian for management and federation of user identities, Grid Trust Service for maintaining and provisioning a federated trust fabric within the Grid environment, and Grid Grouper for enforcing authorization policies based on both local and Grid-level groups. RESULTS The GAARDS infrastructure is available as a stand-alone system and as a component of the caGrid infrastructure. More information about GAARDS can be accessed at http://www.cagrid.org. CONCLUSIONS GAARDS provides a comprehensive system to address the security challenges associated with environments in which resources may be located at different sites, requests to access the resources may cross institutional boundaries, and user credentials are created, managed, revoked dynamically in a de-centralized manner.

GridIMAGE: a novel use of grid computing to support interactive human and computer-assisted detection decision support.

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols.

A distributed data management middleware for data-driven application systems

A key challenge in supporting data-driven scientific applications is the storage and management of input and output data in a distributed environment. We describe a distributed storage middleware, based on a data and metadata management framework, to address this problem. In this middleware system, applications define the structure of their input and output data using XML schemas. The system provides support for 1) registration, versioning, management of schemas, and 2) management of storage, querying, and retrieval of instance data corresponding to the schemas in distributed databases. We carry out an experimental evaluation of the system on a set of PC clusters connected over wide- (WANs) and local-area networks (LANs).

e-Science, caGrid, and Translational Biomedical Research

Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.

XML database support for distributed execution of data-intensive scientific workflows

In this paper we look at the application of XML data management support in scientific data analysis workflows. We describe a software infrastructure that aims to address issues associated with metadata management, data storage and management, and execution of data analysis workflows on distributed storage and compute platforms. This system couples a distributed, filter-stream based dataflow engine with a distributed XML-based data and metadata management system. We present experimental results from a biomedical image analysis use case that involves processing of digitized microscopy images for feature segmentation.