Abstract : This paper presents a novel way to introduce self-configuration and self-optimization autonomic characteristics to algorithmic skeletons using event driven programming techniques. Based on an algorithmic skeleton language, we show that the use of events greatly improves the estimation of the remaining computation time for skeleton execution. Events allow us to precisely monitor the status of the execution of algorithmic skeletons. Using such events, we provide a framework for the execution of skeletons with a very high level of adaptability. We focus mainly on guaranteeing a given execution time for a skeleton, by optimizing autonomically the number of threads allocated. The proposed solution is independent from the platform chosen for executing the skeleton for example we illustrate our approach in a multicore setting, but it could also be adapted to a distributed execution environment.