Grid Technology & Applications: Recent Developments

Grid computing involves the sharing of distributed resources such as supercomputers, clusters, storage, instrumentation and people. Such resources are typically identified with different organisations and are made available to others through a computational middleware. The construction and development of such middlewares is the fundamental idea behind the term Grid Computing. The idea of a computational grid is analogous to an electric power network (grid(, where power generators are distributed, but the users are able to access electric power without bothering about the source of energy and its location. Applications developers need to be able to use vast amounts of computing power quickly and easily, without the huge investment in infrastructure. Over the last number of years advances have been made in this area of computing, such that it is now feasible to create, deploy, and execute large scale applications. Such applications can be either compute intensive or data intensive, or indeed a combination of both. Supporting applications within the different Grid computing systems is providing interesting and challenging aspects for future development of such systems. As our understanding of the applications being developed evolves, so does the requirements of the Grid systems selected to execute these tasks. The aim of this book is to provide an insight into some of the current developments in Grid technologies and applications that execute on these technologies and can be used by graduate/postgraduate students, researchers, and educators. It can also serve as a good reference book for the practitioners in this field. The book consists of a collection of articles from experts in their fields. The book is split into these two sections: *Algorithms and Techniques, *Methodology, Middleware and Tools.

Table of Contents:
Preface; Stochastically Based Algorithms in Lightweight Grid Environments; Data Partitioning, Scheduling and Coordination in Large-Scale Distributed Systems. Case Study: A Pipelined Data Staging Model for Optimal Data Partitioning on Multiple Parallel; Data Replications in Data Grids; Multi-criteria Mapping Techniques for Pipeline Workflows on Heterogeneous platforms; Cycle-scavenging in Grid computing; An Open Source Toolkit for Shared Memory Programming on Computational Grids; High performance Gridrpc Middleware; Fault Tolerance in Grid Workflow Enactment Engines; Design of Grids for Mobile Thin Client Computing; Towards a Robust and Reliable Grid Middleware; Agent-based Discovery, Composition and Orchestration of Grid Web Service; Index.