Investigation on resource optimized job scheduling techniques in mobile grid computing

Abstract

In computer networks an effective grid network is formed with a collection of distributed resources that include computing, storage, and network resources from various locations. Through a grid network, cost savings are achieved with less time consumption and high collaboration of resources among jobs. Grid resources are effectively utilized for the user jobs which are completed with the application of many scheduling techniques. Scheduling refers to the process of choosing which job is to be executed next. Mobility aware job grouping and energy consumption while scheduling the jobs are considered in effective grids. Additionally, virtualization technology is utilized on the dynamic resources that are managed to improve the newlinescheduling performance of the grid. Traditional Adaptive Workflow Scheduling algorithm was employed for the grid applications with workflow tasks where scheduling, resource monitoring, and rescheduling are performed with less amount of time. But, it is failed to deal with dynamic resource availability among present running tasks and residual unexecuted tasks. Job grouping according to their priorities and Grid machines grouping according to their configuration were performed prior to applying the Grid scheduling algorithms. However, improving job processing speed and reducing energy consumption for completing the jobs was not achieved. Mobile resources were clustered with the help of centralized job schedulers according to their connection time, hence the amount of finished jobs were increased. Available energy within a cluster is effectively utilized for processing the CPU-bound jobs by implementing Battery-aware criteria. newline newline