Trace-based performance analysis of scheduling bags of tasks in grids

Grid computing promises large scale computing facilities based on distributed systems. Much research has been done on the subject of increasing the performance of grids. We believe that an adequate performance analysis of grids requires knowledge of the workload and the architecture of the grid. Currently, researchers assume that grids are similar to other distributed systems, such as massively parallel computers. However, workloads in grids differ from other distributed systems, because they consist for a significant part of bags-of-tasks. This research presents a method to model the workload of grids realistically, which enables us to analyze the performance of those systems. We have created a flexible workload model that is specifically tailed for grids. The model explicitly handles bag-of-tasks, which comprise the majority of grid workloads. This workload model has been built using a vast amount of workload trace data from seven real-world grids. The workload model enables us to conduct a performance analysis in which we analyze the impact of several workload characteristics, task selection and scheduling policies, and resource management architectures on system performance. We use simulations to systematically and realistically investigate the system performance in various scenarios. This research has resulted in a grid performance analysis toolbox, a software package that allows researchers to analyze, model, and generate workloads of grids. In addition, we have contributed trace data and analysis to the community by means of the Grid Workloads Archive, an on-line archive of trace data analyzed with our toolbox.Electrical Engineering, Mathematics and Computer Scienc