  We consider online scheduling on unrelated (heterogeneous) machines in a
speed-oblivious setting, where an algorithm is unaware of the exact
job-dependent processing speeds. We show strong impossibility results for
clairvoyant and non-clairvoyant algorithms and overcome them in models inspired
by practical settings: (i) we provide competitive learning-augmented
algorithms, assuming that (possibly erroneous) predictions on the speeds are
given, and (ii) we provide competitive algorithms for the speed-ordered model,
where a single global order of machines according to their unknown
job-dependent speeds is known. We prove strong theoretical guarantees and
evaluate our findings on a representative heterogeneous multi-core processor.
These seem to be the first empirical results for scheduling algorithms with
predictions that are evaluated in a non-synthetic hardware environment.
