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Space-Efficient Scheduling of Stochastically Generated Tasks

Stefan Kiefer ( Technische Universität München )

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 d tasks for each number d. 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 also bounds on the expectation of S^sigma.

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