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Resource allocation for high-performance real-time applications is challenging due to the applications' data-dependent nature, dynamic changes in their external environment, and limited resource availability in their target embedded system platforms. These challenges may be met by use of adaptive resource allocation (ARA) mechanisms that can promptly adjust resource allocation to changes in an application's resource needs, whenever there is a risk of failing to satisfy its timing constraints. By taking advantage of an application's adaptation capabilities, ARA eliminates the need for 'over-sizing' real-time systems to meet worst-case application needs. This paper proposes a model for describing an application's adaptation capabilities and the runtime variation of its resource needs. The paper also proposes a satisfiability-driven set of performance metrics for capturing the impact of ARA mechanisms on the performance of adaptable real-time applications. The relevance of the proposed set of metrics is demonstrated experimentally, using a synthetic application designed to represent time-critical applications in C31 systems.