Table 8 Comparison of task scheduling methods

Monte Carlo Simulation Method

High precision to get the best schedule. The Monte Carlo method reduces the memory requirements of the fixed short scheduling period, resulting in high system throughput.

High simulation work with exhaustive searches for optimization. This method does not make the adapt to sudden changes in workload. Longer planning horizons degrade performance.

Blind Pick Scheduling Method

With moderate overhead, this method applies a reduced search space and can somewhat adapt to rapid workload fluctuations.

It has moderate accuracy because it has less overhead. With a bad selection set, the performance drops in Monte Carlo.

Ordinal Optimization (Proposed) Method

With very little overhead, OO can adapt to fast workload fluctuations and run suboptimal schedules with high multitasking throughput and reduced memory footprint.

The suboptimal schedule generated at each period may not be as optimal as the schedule generated by the Monte Carlo method. A high noise level can degrade the schedule generated by OO.