An adaptive job allocation method for multicomputer systems

Abstract

The fragmentation problem in multicomputer systems reduces the system utilization and prohibits the systems from performing at their full capacity. In this paper, we propose a generic job allocation method for multicomputer systems based on job size reduction. We reduce the subsystem size requirement adaptively according to the availability of processors. The fragmentation problem is greatly alleviated by this approach. To ensure that the benefit of reducing fragmentation is not outweighed by the penalty of executing jobs on less number of processors, we restrict the number of times the size of a job can be reduced; hence the name restricted size reduction (RSR). Extensive simulations are conducted to validate the RSR method for hypercubes and mesh-based systems with different allocation algorithms. It is observed in both mesh and hypercube that by using the RSR method a simple algorithm can provide better performance than the more sophisticated allocation algorithms. We have also compared RSR method with the limit allocation that is based on a similar idea. Our method outperforms the limit allocation and provides better fairness to different size jobs. The performance gain, fairness, and low complexity makes the RSR method highly attractive.