  We present a flexible framework for the automated competitive analysis of
on-line scheduling algorithms for firm-deadline real-time tasks based on
multi-objective graphs: Given a taskset and an on-line scheduling algorithm
specified as a labeled transition system, along with some optional safety,
liveness, and/or limit-average constraints for the adversary, we automatically
compute the competitive ratio of the algorithm w.r.t. a clairvoyant scheduler.
We demonstrate the flexibility and power of our approach by comparing the
competitive ratio of several on-line algorithms, including $D^{over}$, that
have been proposed in the past, for various tasksets. Our experimental results
reveal that none of these algorithms is universally optimal, in the sense that
there are tasksets where other schedulers provide better performance. Our
framework is hence a very useful design tool for selecting optimal algorithms
for a given application.
