Scheduling conditional task graphs

Authors:
Michele Lombardi;Michela Milano
Affiliations:
DEIS, University of Bologna, Bologna, Italy;DEIS, University of Bologna, Bologna, Italy
Venue:
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Year:
2007

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Abstract

This paper describes a complete and efficient solution to the scheduling of conditional task graphs whose nodes are activities and whose arcs can be labelled with a probability distribution. Tasks are executed if a set of conditions hold at scheduling execution time. The model is therefore stochastic. The minimization of the expected makespan implies an exponential-sized description of the objective function. We propose an analytic formulation of the stochastic objective function based on the task graph analysis, and a conditional constraint that handles it efficiently. Experimental results show the effectiveness of our approach in comparison with (1) an approach using a deterministic objective function and (2) scenario based constraint programming taking into account all scenarios or only a part of them.