Parallel Computer Workload Modeling with Markov Chains

In order to evaluate di#erent scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload that is served on the parallel machine, a representative workload model is required. Common approaches such as using a probability distribution model can capture the static feature of real workloads, but they do not consider the temporal relation in the traces. In this paper, a workload model is presented which uses Markov chains for modeling job parameters. In order to consider the interdependence of individual parameters without requiring large scale Markov chains, a novel method for transforming the states in di#erent Markov chains is presented. The results show that the model yields closer results to the real workloads than other common approaches

User Group-based Workload Analysis and Modeling

Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or Grid components. As the scheduling quality highly depends on the characteristics of the workload running on such resources, a representative workload model is significant for performance evaluation. Previous approaches on workload modelling mainly focused on methods that use statistical distributions to fit the overall workload characteristics. Therefore, the individual association and correlation to users or groups are usually lost. However, job scheduling for single parallel installations as well as for Grid systems started to focus more on the quality of service for specific user groups. Here, detailed knowledge of the individual user characteristic and preference is necessary for developing appropriate scheduling strategies. In the absence of a large information base of actual workloads, the adequate modelling of submission behaviors is sought. In this paper, we propose a new workload model, called MUGM (Mixed User Group Model), which maintains the characteristics of individual user groups. The MUGM method has been further evaluated by simulations and shown to yield good results.

Benefits of global grid computing for job scheduling

Abstract — In addition to other advantages, computational Grids are considered to utilize the participating compute resources more efficiently as well as to improve the response time for user jobs. Due to the lack of common large scale global Grids and corresponding studies on Grid workloads this assumption is not yet verified. In this paper, the effect of geographical distribution of Grid resources on the machine utilization and the average response time is analyzed. To this end, simulations have been performed. The results show a significant benefit for the job scheduling quality due to the participation in a true global Grid. The average weighted response times of all submitted jobs decrease up to about 30%. The results have been verified using different workloads and Grid configurations. I