In computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence fault tolerance plays a key role in grid to avoid the problem of unreliability. Scheduling the task to the appropriate resource is a vital requirement in computational Grid. The fittest resource sched...

Data Grid is a type of Grid Computing systems whichis designed to provide geographically distributed data resources to large computational problems that require mining and evaluating large amounts of data. Managing this data in a centralized location increases the data access time and hence much time is taken to execute the job. So to reduce the data access time, "Replication" is used. Data rep...

Main characteristics of desktop grids are the large number of nodes and their heterogeneity. Application speedup on a large-scale desktop grid is limited by the heterogeneous computational capabilities of each node, which increase the synchronization overhead, and by the large number of nodes, that results in the serial fraction dominating performance. In this paper we present an innovative tec...

Fault tolerance is an important property for large scale computational grid systems, where geographically distributed nodes co-operate to execute a task. In order to achieve high level of reliability and availability, the grid infrastructure should be a foolproof fault tolerant. Since the failure of resources affects job execution fatally, fault tolerance service is essential to satisfy QOS req...

Data Grid systems require the management of a massive number of data sets to make the data available to the many clients accessing the data. Data is typically replicated in these large scale applications to improve the data availability and job response time. Because the behavior of the thousands of Grid users is very dynamic, it is a challenging problem to determine where and when to make data...

Many variables in surveys follow natural orderings that should be respected in estimates of domain means. For instance, the National Compensation Survey estimates mean wages for many job categories, and these mean wages are expected to be non-decreasing according to job level. In this type of situation, isotonic regression can be applied to give constrained estimators satisfying the monotonicit...

The next generation of international scientific projects poses problems for current computation and data handling techniques, and Grid computing is seen as a viable solution. Possible data handling strategies should be tested before choosing which is deployed on a real Grid; to this end, the Grid simulator OptorSim is presented. A selection of results for some job scheduling and data replicatio...

In high-energy physics, bioinformatics, and other disciplines, we encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets. So-called Data Grids seek to harness geographically distributed resources for such large-scale data-intensive problems. Yet effective scheduling in such environments is challenging, due to a need to address a variety of ...

In grid computing, resources are used outside the boundary of organizations and it becomes increasingly difficult to guarantee that resources being used are not malicious. Also, resources may enter and leave the grid at any time. So, fault tolerance is a crucial issue in grid computing. Fault tolerance can enhance grid throughput, utilization, response time and more economic profits. All mechan...

In this paper, we present an approach to safety scheduling in distributed computing based on strategies of resource co-allocation for complex sets of tasks (jobs). The necessity of guaranteed job execution until the time limits requires taking into account the distributed environment dynamics, namely, changes in the number of jobs for servicing, volumes of computations, possible failures of pro...