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The authors study the problem of scheduling periodic real-time tasks which have individual minimum reward requirements. They consider situations where tasks generate jobs that can be provided arbitrary service times before their deadlines, and obtain rewards based on the service times received by the jobs of the task. They show that this model is compatible with the imprecise computation models and the increasing reward with increasing service models. In contrast to previous work on these models, which mainly focus on maximizing the total reward in the system, they additionally aim to fulfill different reward requirements of different tasks. This provides better fairness and also allows fine-grained tradeoff between tasks.
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