Grid Computing

The emerging grid technology provides a secured platform for multidisciplinary experts in the security intelligence profession to collaborate and fight global terrorism. This chapter developed grid architecture and implementation strategy on how to connect the dots between security agents such as the CIA, FBI, police, custom officers and transport industry to share data and information on terrorists and their movements. The major grid components that featured in the architecture are the grid security portal, data grid, computational grid, semantic grid and collaboratory. The challenges of implementing this architecture are conflicting laws, cooperation among governments, and information on terrorist’s network and interoperability problem.

A Framework for Semantic Grid in E-Science

A framework is proposed that creates, uses, and communicates information, whose organizational dynamics allows performing a distributed cooperative enterprise in public environments, even over open source systems. The approach assumes the web services as the enacting paradigm possibly over a grid, to formalize interactions as cooperative services on various computational nodes of a network. The illustrated case study shows that some portions, both of processes and of data or knowledge, can be shared in a collaborative environment, which is also more generally true for any kind of either complex or resource demanding (or both) interaction that will benefit any of the approaches.

Developing Biomedical Applications in the Framework of EELA

In the last years an increasing demand for Grid Infrastructures has resulted in several international collaborations. This is the case of the EELA Project, which has brought together collaborating groups of Latin America and Europe. One year ago, the authors presented this e-infrastructure used, among others, by the biomedical groups for the studies of oncological analysis, neglected diseases, sequence alignments and computational phylogenetics. After this period, the achieved advances and the scientific results are summarized in this chapter

An Architectural Overview of the GRelC Data Access Service

Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed environment. Computational Grid allows running millions of jobs in parallel, but the huge amount of generated data has caused another interesting problem: the management (classification, storage, discovery etc.) of distributed data, i.e., a Data Grid specific issue. In the last decade, many efforts concerning the management of data (grid-storage services, metadata services, grid-database access and integration services etc.) identify data management as a real challenge for the next generation petascale grid environments. This work provides an architectural overview of the GRelC DAS, a grid database access service developed in the context of the GRelC Project and currently used for production/tutorial activities both in gLite and Globus based grid environments.

Consistency of Replicated Datasets in Grid Computing

Data replication is a well-known technique used in distributed systems in order to improve fault tolerance and make data access faster. Several copies of a dataset are created and placed at different nodes, so that users can access the replica closest to them, and at the same time the data access load is distributed among the replicas. In today’s Grid middleware solutions, data management services allow users to replicate datasets (i.e., flat files or databases) among storage elements within a Grid, but replicas are often considered read-only because of the absence of mechanisms able to propagate updates and enforce replica consistency. This entry analyzes the replica consistency problem and provides hints for the development of a Replica Consistency Service, highlighting the main issues and pros and cons of several approaches.

Data-Aware Distributed Batch Scheduling

As the data requirements of scientific distributed applications increase, the access to remote data becomes the main performance bottleneck for these applications. Traditional distributed computing systems closely couple data placement and computation, and consider data placement as a side effect of computation. Data placement is either embedded in the computation and causes the computation to delay, or performed as simple scripts which do not have the privileges of a job. The insufficiency of the traditional systems and existing CPU-oriented schedulers in dealing with the complex data handling problem has yielded a new emerging era: the data-aware schedulers. This chapter discusses the challenges in this area as well as future trends, with a focus on Stork case study.

Overview of Grid Computing

This chapter presents a historical record of the advent of Grid with a recourse to some basic definitions commonly accepted by most researchers. It discusses the current and potential users of Grid computing and the expected changes in the user base as it gains popularity. The role of the Internet infrastructure in shaping the grid evolution received detailed treatment. Furthermore, the chapter contrasts grid computing with distributed and peer-to-peer computing and highlighted the salient features. Finally, the chapter discusses the recent advances in Web and Grid service technologies, including international projects, emerging standards and organizations, and the current challenges faced by Grid researchers.

Grid Computing Initiatives in India

Efforts in Grid Computing, both in academia and industry, continue to grow rapidly worldwide for research, scientific and commercial purposes. Building a commanding position in Grid computing is crucial for India. The major Indian National Grid Computing initiative is GARUDA. Other major efforts include the BIOGRID and VISHWA. Several Indian IT companies too are investing a lot into the research and development of grid computing technology. Though grid computing is presently at a fairly nascent stage, it is seen as a cutting edge technology. This chapter presents the state-of-the-art of grid computing technology and the India’s efforts in developing this emerging technology.

Accessing Grid Metadata through a Web Interface

This chapter focuses on the efforts to design and develop a standard pure Java API to access the metadata service of the EGEE Grid middleware, and provide at the same time a powerful object oriented framework to allow engineers and programmers to embed metadata features inside their own application, using a standard approach based on design patterns. A specific web interface is built on top of this framework that permits users and administrators to manage the metadata catalog, from any platform and everywhere, according to their own X.509-based credentials.

GIS Grids and the Business Use of GIS Data

Grid computing is becoming as essential part of different business analysis. In traditional business computing infrastructures data transfer occurs to and from computing resources at the network edges. On the other hand, most business activities are bound to space and location. The aim of this chapter is to describe the business use of geographic data (business intelligence) and Geographic Information System (GIS) grids. As conclusion business intelligence helps to improve productivity by giving users information they need when they need it most at the point of decision. Organizations that effectively use geographic information elements analyzing their risk portfolio and compliance activities can reduce costs and increase the clarity of their operations. Grid computing is an answer to the needs of efficient GIS aided analysis. When geographic data, grid computing and business information are combined, they create new possibilities to enhance and broaden the standpoints of already existing data within organizations.