Simulation-based average case analysis for parallel job scheduling

This paper analyses the resource allocation problem in parallel job scheduling, with emphasis given to gang service algorithms. Gang service has been widely used as a practical solution to the dynamic parallel job scheduling problem. To provide a sound analysis of gang service performance, a novel methodology based on the traditional concept of competitive ratio is introduced. Dubbed dynamic competitive ratio, the new method is used to do an average case analysis based on simulation of resource allocation algorithms. These resource allocation algorithms apply to the gang service scheduling of a workload generated by a statistical model. Moreover, dynamic competitive ratio is the figure of merit used to evaluate and compare packing strategies for job scheduling under multiple constraints. It is shown that for the unidimensional case there is a small difference between the performance of best fit and first fit; first fit can hence be used without significant system degradation. For the multidimensional case, when memory is also considered, we conclude that the resource allocation algorithm must try to balance the resource utilization in all dimensions simultaneously, instead of given priority to only one dimension of the problem