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 algorithms with predictions that are performed in a non-synthetic environment on real hardware.

翻译：我们考虑在速度无关设置下，异构（非同质）机器上的在线调度问题，其中算法无法获知与作业相关的精确处理速度。我们首先展示了有视及无视算法均存在强不可能性结果，并在受实际场景启发的模型中克服了这些局限：（i）我们提出了具有竞争性的学习增强算法，假设给定（可能包含误差的）速度预测；（ii）针对速度有序模型，我们提出了具有竞争性的算法，其中已知根据未知作业相关速度对机器形成的单一全局顺序。我们证明了强理论保证，并在代表性异构多核处理器上评估了我们的研究结果。这似乎是首个在真实硬件非合成环境中执行的带有预测算法的经验性结果。