Data grid systems are utilized to share, manage, and process large data sets. On the other
hand, an increasing number of applications with real-time constraints arise in several …

In recent years, grid computing has emerged as one of the main computational platforms to
perform many extremely difficult and/or time-consuming tasks. The amount of data …

In the Data Grid environment, the primary goal of data replication is to shorten the data
access time experienced by the job and consequently reduce the job turnaround time. After …

Real-time Grid applications are emerging in many disciplines of science and engineering. In
order to run these applications while meeting the associated real-time constraints with them …

In most Grid systems, submitted jobs are initially placed into a queue if there are no
available compute nodes. Therefore, there is no guarantee as to when these jobs will be …

Data Grid is a geographically distributed environment that deals with large-scale data-
intensive applications. Effective scheduling in Grid can reduce the amount of data …

Data Grid Environments (DGEs) are a geographically distributed system that are emerging
as the solution of data-intensive applications in scientific and engineering enterprise …

Data Grids seek to harness geographically distributed resources for large-scale data-
intensive problems such as those encountered in high energy physics, bioinformatics, and …

Data grids seek to harness geographically distributed resources for large-scale data-
intensive problems. Such problems involve loosely coupled jobs and large data sets mostly …

In large‐scale data‐intensive applications data play a pivotal role in the execution of these
applications, and data transfer is a primary cause of job execution delay. In environments …