As computer networks increase in size, become more heterogeneous and span greater geographic distances, applications must be designed to cope with the very large scale, poor reliability, and often, with the extreme dynamism of the underlying network. Aggregation is a key functional building block for such applications: it refers to a set of functions that provide components of a distributed system access to global information including network size, average load, average uptime, location and description of hotspots, and so on. Local access to global information is often very useful, if not indispensable for building applications that are robust and adaptive. For example, in an industrial control application, some aggregate value reaching a threshold may trigger the execution of certain actions; a distributed storage system will want to know the total available free space; load-balancing protocols may benefit from knowing the target average load so as to minimize the load they transfer. We propose a gossip-based protocol for computing aggregate values over network components in a fully decentralized fashion. The class of aggregate functions we can compute is very broad and includes many useful special cases such as counting, averages, sums, products, and extremal values. The protocol is suitable for extremely large and highly dynamic systems due to its proactive structure—all nodes receive the aggregate value continuously, thus being able to track

In the hope of stimulating discussion, we present a heuristic decision tree that designers can use to judge how suitable a P2P solution might be for a particular problem. It is based on characteristics of a wide range of P2P systems from the literature, both proposed and deployed. These include budget, resource relevance, trust, rate of system change, and criticality.

The Grid is rapidly emerging as the means for coordinated resource sharing and problem solving in multi-institutional virtual organizations while providing dependable, consistent, pervasive access to global resources. The emergence of computational Grids and the potential for seamless aggregation and interactions between distributed services and resources, has led to the start of new era of computing. Tremendously large number and the heterogeneous nature of grid computing resource make the resource management a significantly challenging job. Resource management scenarios often include resource discovery, resource monitoring, resource inventories, resource provisioning, fault isolation, variety of autonomic capabilities and service level management activities. Out of these scenarios, fault tolerance is one of the main research areas. The probability of fault occurrence increases, as the number of resources involved in grid increases. Till today there is no system that can be fully fault tolerant. In this research our main focus is on the development of fault tolerance system for

Grid computing in general comes from high-performance computing, super computing and later cluster computing where several processors or work stations are connected via a high-speed interconnect in order to compute a mutual program. Originally, the cluster was meant to span a local area network but then it was also extended to the wide area. A Grid itself is supposed to connect computing resources over the wide area network. The Grid research field can further be divided into two large sub-domains: Computational Grid and Data Grid. Whereas a Computational Grid is a natural extension of the former cluster computer where large computing tasks have to be computed at distributed computing resources, a Data Grid deals with the efficient management, placement and replication of large amounts of data. However, once data are in place, computational tasks can be run on the Grid using the provided data. Data grids act as intermediaries between the compute grids and the data ( real world information). Design of these data grids is critical as data and not computation is the heart of business or scientific application. Data grids are still maturing to provide complex services and ensuring data availability over the internet. Here we discuss these data grids and argue that traditional computation focused design of systems is passe. We look afresh at the data grid design and try to provide solutions for improving them.

. This paper describes mathematical and software developments for a suite of programs for solving ordinary di#erential equations in Matlab. Key words. ordinary di#erential equations, sti# systems, BDF, Gear method, Rosenbrock method, non-sti# systems, Runge-Kutta method, Adams method, software AMS subject classifications. 65L06, 65L05, 65Y99, 34A65 1. Introduction. This paper presents mathematical and software developments that are the basis for a suite of programs for the solution of initial value problems y # = F (t, y) on a time interval [t 0 ,t f ], given initial values y(t 0 )=y 0 . The solvers for sti# problems allow the more general form M(t) y # = f(t, y) with a mass matrix M(t) that is non-singular and (usually) sparse. The programs have been developed for Matlab [29], a widely used environment for scientific computing. This influenced the choice of methods and how they were implemented. As in many environments, the typical problem in Matlab is solved interactively and...

In this paper, we show how to use a Relational Database Management System in support of Finite Element Analysis. We believe it is a new way of thinking about data management in well-understood applications to prepare them for two major challenges, - size and integration (globalization). Neither extreme size nor integration (with other applications over the Web) was a design concern 30 years ago when the paradigm for FEA implementation first was formed. On the other hand, database technology has come a long way since its inception and it is past time to highlight its usefulness to the field of scientific computing and computer based engineering. This series aims to widen the list of applications for database designers and for FEA users and application developers to reap some of the benefits of database development.

CONTENTS 1. IntroducOHk ....................................... 1 2. Charac5xOwOMfi5 of LWG ................................ 2 3. A Comparison of Jini and JXTA .......................... 4 3.1. Vision ........................................ 4 3.2. Sc.5 ........................................ 5 3.3. Resourc Discc5x andPublicM5xg .................... 5 3.4.Servic Interacg5x ............................... 8 3.5. GroupConc5w .................................. 10 3.6.Sec5wUB ....................................... 10 3.7. Flexibility ..................................... 11 3.8. Ease-of-Use .................................... 11 4. Contributions of Jini and JXTA to an LWG Middleware .......... 11 4.1. Limited Administrative Resourc5 ...................... 11 4.2. Heterogeneity ................................... 12 4.3. Volatility ...................................... 12 4.4. Sc.5 ........................................ 13 4.5. Openness ..................................... 13 4.6. H

The biomedical ontologies contain the complex distributed heterogeneous data, to analyze and process this data is the big challenge for biomedical communities. The common goal of biomedical communities is to annotate this data. These problems generated a need to use the services of grid on semantic web. Semantic Grid is the integration of Grid with the Semantic web, which will play the vital role in future web. The semantic grid architecture provides semantic and knowledge support. In this paper we discuss two biomedical ontologies, Biological Viruses Community Ontology (BVCO) and the most mature Gene Ontology (GO).