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^sigma modeling the maximal space needed by the processor to store the currently active tasks when acting under the scheduler sigma. We obtain tail bounds for the distribution of S^sigma for both offline and online schedulers, and investigate the expected value of S^sigma.
@article{arxiv.1004.4286,
title = {Space-efficient scheduling of stochastically generated tasks},
author = {Tomáš Brázdil and Javier Esparza and Stefan Kiefer and Michael Luttenberger},
journal= {arXiv preprint arXiv:1004.4286},
year = {2010}
}