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.

翻译：我们考虑在速度无关设定下，基于无关（异构）机器的在线调度问题——算法无法获知依赖于作业的精确处理速度。我们展示了针对预知型与非预知型算法的强不可能性结果，并在受实际场景启发的模型中克服了这些限制：（i）假设提供了（可能存在误差的）速度预测，我们设计了具有竞争力的学习增强型算法；（ii）针对速度有序模型（即已知机器依据未知的作业依赖速度形成的单一全局顺序），我们提出了具有竞争力的算法。我们证明了强理论保障，并在代表性异构多核处理器上评估了实验结果。这似乎是首个在非合成硬件环境下对带预测的调度算法进行实证评估的研究。