Complex systems composed of multi-level hierarchical systems exhibit complex interactingrndependences which affects the performance of the overall system. Although, one solution is to tightlyrnmonitor each system activity and feedback to the next step level in order to take appropriate measures andrnactions to improve the system this solution is costly and too systematic. In addition, a dependency chainrnexists between low level systems and higher level systems this dependency chains being as long as thernnumber of levels of the system. We propose in this paper an activity driven adaptive hierarchical predictionrntechnique for complex systems which minimizes monitoring and prediction resources requirements andrnstill keep efficient overall systems performance prediction.
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