Space-efficient scheduling of stochastically generated tasks

Abstract

We study the problem of scheduling tasks for execution by a processor when the tasks can stochastically generate new tasks. Tasks can be of different types, and each type has a fixed, known probability of generating other tasks. We present results on the random variable S σ modeling the maximal space needed by the processor to store the currently active tasks when acting under the scheduler σ. We obtain tail bounds for the distribution of S σ for both offline and online schedulers, and investigate the expected value . © 2010 Springer-Verlag Berlin Heidelberg.