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Feedback control techniques have recently been applied to a variety of real-time systems. However, a fundamental issue that was left out is guaranteeing system controllability and the feasibility of applying feedback control to such systems. No control algorithms can effectively control a system which itself is uncontrollable or infeasible. In this paper, we use the multiprocessor utilization control problem as a representative example to study the controllability and feasibility of distributed real-time systems. We prove that controllability and feasibility of a system depend crucially on end-to-end task allocations. We then present algorithms for deploying end-to-end tasks to ensure that the system is controllable and utilization control is feasible for the system. Furthermore, we develop runtime algorithms to maintain controllability and feasibility by reallocating tasks dynamically in response to workload variations, such as task terminations and migrations caused by processor failures. We implement our algorithms in a robust real-time middleware system and report empirical results on an experimental test-bed. We also evaluate the performance of our approach in large systems using numerical experiments. Our results demonstrate that the proposed task allocation algorithms improve the robustness of feedback control in distributed real-time systems.