This paper, in the spirit of the new area of machine learning augmented algorithms, attempts to obtain the best of both worlds for the classical, deadline based, online speed-scaling problem: Based on the introduction of a novel prediction setup, we develop algorithms that (i) obtain provably low energy-consumption in ...
Dec 6, 2021 · Machine learning can be a useful approach in practice by predicting the future load of the system based on, for example, historical data.
Machine learning can be a useful approach in practice by predicting the future load of the system based on, for example, historical data. However, the ...
Dec 6, 2021 · In this context, the main contribution of the current paper is to introduce a natural alternative prediction setup and error measure as well as ...
Dec 6, 2021 · Firstly, we address dynamic speed scaling, where processors can run at variable speed/frequency. The goal is to use the speed spectrum of the ...
Aug 2, 2024 · Antonios Antoniadis, Peyman Jabbarzade, Golnoosh Shahkarami: A Novel Prediction Setup for Online Speed-Scaling. CoRR abs/2112.03082 (2021).
A Novel Prediction Setup for Online Speed-Scaling · no code implementations • 6 Dec 2021 • Antonios Antoniadis, Peyman Jabbarzade Ganje, Golnoosh Shahkarami.
A Novel Prediction Setup for Online Speed-Scaling. Antoniadis, Ganje ... Learning Augmented Energy Minimization via Speed Scaling. Bamas, Maggiori ...
Year · A novel prediction setup for online speed-scaling. A Antoniadis, PJ Ganje, G Shahkarami. arXiv preprint arXiv:2112.03082, 2021. 22, 2021. Dynamic ...
This work considers the first, and most well studied, speed scaling problem in the algorithmic literature: where the scheduling quality of service measure ...