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 \(\mathbb{E}{S^{\sigma}}\).
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Brázdil, T., Esparza, J., Kiefer, S., Luttenberger, M. (2010). Space-Efficient Scheduling of Stochastically Generated Tasks. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds) Automata, Languages and Programming. ICALP 2010. Lecture Notes in Computer Science, vol 6199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14162-1_45
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