In systems where job service demands are only known probabilistically, there is very little to distinguish between jobs. Therefore, no universal optimum scheduling strategy or algorithm exists. If the distribution of job times is known, then the residual time (expected time remaining for a job), based on the service it has already received, can be calculated. In a detailed discrete event simulation, we have explored the use of this function for increasing the probability that a job will meet its deadline. We have tested many different distributions with a wide range of 2 s and shape, four of which are reported here. We compare with RR and FCFS, and find that in all distributions studied our algorithm performs best. We also studied the use of two slow servers versus one fast server, and have found that they provide comparable performance, and in a few cases the double server system does better.