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Dynamic real-time systems such as phased-array radars must manage multiple resources, satisfy energy constraints and make frequent on-line scheduling decisions. These systems are hard to manage because task and system requirements change rapidly (e.g. in radar systems, the targets/tasks in the sky are moving continuously) and must satisfy a multitude of constraints. Their highly dynamic nature and stringent time constraints lead to complex cross-layer interactions in these systems. Therefore, the design of such systems has long been a conservative and/or unpredictable mixture of pre-computed schedules, pessimistic resource allocations, cautious energy usage and operator intuition. In this paper, we present an integrated approach that simultaneously maximizes overall system utility, performs task scheduling and satisfies multi-resource constraints. Using a phased-array radar system, we show that our approach can reconfigure settings of 100 tracks at every 0.7 sec in real-time, and performs within 0.1% of the achievable optimal solution.