Simulations of three adaptive, decentralized controlled, job scheduling algorithms

Abstract

Simulation results of three adaptive, decentralized controlled job scheduling algorithms which assume absolutely no a priori knowledge about jobs are presented. The results provide insight into the workings and relative effectiveness of the three algorithms, as well as insight into the performance of a special type of decentralized control. The simulation approach includes tuning the parameters of each algorithm, and then comparing the three algorithms based on response time, load balancing and the percentage of job movement. Each of the algorithm is compared under light, moderate, and heavy loads in the system, as well as a function of the traffic in the communication subnet and the scheduling interval. Modifications to the models are then introduced to further investigate the impact of various other parameters such as the cost of the scheduling algorithm itself, the effect of highly degraded state information and eliminating the movement of large (in size) jobs. Three simple analytical models are also presented and compared to the simulation results. A general observation is that, if tuned correctly, the decentralized algorithms exhibit stable behavior and considerably improve performance (response time and load balancing) at modest cost (percentage of job movement and algorithm execution cost).