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Found 4 papers, 0 papers with code
Energy-Efficient Scheduling with Predictions
We show that, when the prediction error is small, this framework gives improved competitive ratios for many different energy-efficient scheduling problems, including energy minimization with deadlines, while also maintaining a bounded competitive ratio regardless of the prediction error.
Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow.
Online TSP with Predictions
We initiate the study of online routing problems with predictions, inspired by recent exciting results in the area of learning-augmented algorithms.
Scheduling with Speed Predictions
In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initially unknown.
