DSMar 18
A Simpler Analysis for $\varepsilon$-Clairvoyant Flow Time Scheduling
arXiv:2603.1754267.4h-index: 10
AI Analysis
This work provides a clearer theoretical foundation for an existing scheduling algorithm, which is incremental in nature.
The paper simplifies the proof that the Shortest Lower-Bound First (SLF) algorithm is optimal for minimizing total flow time in the ε-clairvoyant scheduling setting, confirming its theoretical correctness without new performance numbers.
We simplify the proof of the optimality of the Shortest Lower-Bound First (SLF) algorithm, introduced by Gupta, Kaplan, Lindermayr, Schlöter, and Yingchareonthawornchai [FOCS'25], for minimizing the total flow time in the $\varepsilon$-clairvoyant setting.