  Contract scheduling is a widely studied framework for designing real-time
systems with interruptible capabilities. Previous work has showed that a
prediction on the interruption time can help improve the performance of
contract-based systems, however it has relied on a single prediction that is
provided by a deterministic oracle. In this work, we introduce and study more
general and realistic learning-augmented settings in which the prediction is in
the form of a probability distribution, or it is given as a set of multiple
possible interruption times. For both prediction settings, we design and
analyze schedules which perform optimally if the prediction is accurate, while
simultaneously guaranteeing the best worst-case performance if the prediction
is adversarial. We also provide evidence that the resulting system is robust to
prediction errors in the distributional setting. Last, we present an
experimental evaluation that confirms the theoretical findings, and illustrates
the performance improvements that can be attained in practice.
