Abstract. In this paper we continue our investigation of stochastic (and hence dynamic) variants of classical scheduling problems. Such problems can be modeled as duration probabilistic automata (DPA), a well-structured class of acyclic timed automata where temporal uncertainty is interpreted as a bounded uniform distribution of task durations [18]. In [12] we have developed a framework for computing the expected performance of a given scheduling policy. In the present paper we move from analysis to controller synthesis and develop a dynamicprogramming style procedure for automatically synthesizing (or approximating) expected time optimal schedulers, using an iterative computation of a stochastic time-to-go function over the state and clock space of the automaton.

Abstract. We show how to evaluate the performance of solutions to finite-horizon scheduling problems where task durations are specified by bounded uniform distributions. Our computational technique, based on computing the volumes of zones, constitutes a contribution to the computational study of scheduling under uncertainty and stochastic systems in general.

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain.