Association rules learning technique for knowledge mining about scheduling algorithm performance

With the advent of increasingly higher numbers of processors on-chip, task scheduling has become an important concern in system design, and research in this area has produced substantial and diversified knowledge. As a result, the efficient management and taping of this knowledge has become a concern in itself. This paper addresses the issue of how to effectively extract performance information about a scheduling algorithm in the context of a set of applications, by learning the association rules between the applications' attributes and the algorithms' performance metrics. The new methodology that is presented serves to both increase the designer's knowledge about a particular scheduling algorithm and compare algorithms.