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Whenever the player moves this allocation, they incur a movement cost defined by the distance metric\n            <jats:italic toggle=\"yes\">d<\/jats:italic>\n            (\u22c5, \u22c5) that captures, e.g., an overhead cost. SOAD formalizes the open problem of combining general metrics and deadline constraints in the online algorithms literature, unifying problems such as metrical task systems and online search. We propose a competitive algorithm for SOAD along with a matching lower bound establishing its optimality. Our main algorithm, ST-CLIP, is a learning-augmented algorithm that takes advantage of predictions (e.g., forecasts of relevant costs) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms in a simulated case study of carbon-aware spatiotemporal workload management, an application in sustainable computing that schedules a delay-tolerant batch compute job on a distributed network of data centers. 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