Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

Resource Characteristic Based Optimization for Grid Scheduling

Scheduling is an active research area in the Computational Grid environment. The objective of grid scheduling is to deliver both the Quality of Service (QoS) requirement of the grid users, as well as high utilization of the resources. To obtain optimal scheduling in the generalized grid environment is an NP-complete problem. A large number of researchers have presented heuristic algorithms to find a near-global optimum for the static scheduling model of the grid. Relatively a smaller number of researchers have worked on the scheduling problem for the dynamic scheduling model. This thesis proposes a new resource characteristic based optimization method, which may be combined with Earlier Gap, Earliest Deadline First (EG-EDF) policy to schedule jobs in a dynamic environment. The proposed algorithm generates near-optimal solutions, which are better than those reported in the literature for a specific range of datasets. Extensive experimentation has proved the efficacy of our method