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Worst case scheduling techniques for real-time applications often result in severe underutilization of the processor resources since most tasks finish in much less time than their anticipated worst-case execution times. In this paper we describe compiler-based techniques that classify the application code on the basis of predictability and monotonicity, introduce measurement code fragments at selected points in the application code, and use the results of run-time measurements to dynamically adapt worst-case schedules. This results in better utilization of the system and early failure detection and recovery.