This paper presents a novel Bee Colony based optimization algorithm, named Job Data
Scheduling using Bee Colony (JDS-BC). JDS-BC consists of two collaborating mechanisms …

In Grids scheduling decisions are often made on the basis of jobs being either data or
computation intensive: in data intensive situations jobs may be pushed to the data and in …

The large-scale distributed dataset searching faced dynamicity, heterogeneity, and latency
issues that emphasize the importance of approach to orchestrate the search operations. The …

Resource discovery in a grid environment is a critical problem, as a typical grid system
includes a very large number of resources, which must be readily identified and accessed to …

Jobs on the Grid require security credentials throughout their run for accessing secure Grid
resources, such as GridFTP data repositories. However, delegating long-lived credentials to …

Results from the research and development of a Data Intensive and Network Aware (DIANA)
scheduling engine, to be used primarily for data intensive sciences such as physics …

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously
schedule jobs and replicate data files to different entities of a grid system so that the overall …

Efficient and robust system for accessing computational resources and managing job
operations is a key component of any Grid framework designed to support large distributed …

In this paper, we introduce a methodology for the approximation of optimal solutions for a
resource allocation problem in the domain of Grid scheduling on High Performance …

This work presents a genetic algorithm (GA)‐based optimization technique, called GA‐
ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in …