no code implementations • NeurIPS 2023 • Eric Balkanski, Noemie Perivier, Clifford Stein, Hao-Ting Wei
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.
no code implementations • 11 May 2023 • Ya-Chun Liang, Clifford Stein, Hao-Ting Wei
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow.
no code implementations • 2 May 2022 • Eric Balkanski, Tingting Ou, Clifford Stein, Hao-Ting Wei
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.