The problem of partially clairvoyant scheduling is concerned with checking whether an ordered set of jobs, having nonconstant execution times and subject to a collection of imposed constraints, has a partially clairvoyant schedule. Variability of
execution times of jobs and nontrivial relationships constraining their executions, are typical features of real-time systems. A partially clairvoyant scheduler parameterizes the schedule, in that the start time of a job in a sequence can depend upon the execution times of jobs that precede it, in the sequence. In real-time scheduling, parameterization of the schedule plays an important role in extending the flexibility of the scheduler, particularly in the presence of variable execution times. It has been shown that the existence of partially clairvoyant schedules can be determined in polynomial time, when the constraints are restricted to be “standard,” that is, relative timing
constraints. In this paper, we extend the class of constraints for which partially clairvoyant schedules can be determined efficiently, to include aggregate constraints. Aggregate constraints form a strict superset of standard constraints and can be used to model performance metrics.
Adaptive Real-Time Scheduling for Soft Tasks with Varying Execution Times
