Association for Computing Machinery
In distributed soft real-time systems, maximizing the aggregate Quality-of-Service (QoS) is a typical system-wide goal, and addressing the problem through distributed optimization is challenging. Subtasks are subject to unpredictable failures in many practical environments, and this makes the problem much harder. In this paper, the authors present a robust optimization framework for maximizing the aggregate QoS in the presence of random failures. They introduce the notion of K-failure to bound the effect of random failures on schedulability. Using this notion they define the concept of K-robustness that quantifies the degree of robustness on QoS guarantee in a probabilistic sense.