DeepAI AI Chat
Log In Sign Up

Learning Augmented Energy Minimization via Speed Scaling

by   Étienne Bamas, et al.

As power management has become a primary concern in modern data centers, computing resources are being scaled dynamically to minimize energy consumption. We initiate the study of a variant of the classic online speed scaling problem, in which machine learning predictions about the future can be integrated naturally. Inspired by recent work on learning-augmented online algorithms, we propose an algorithm which incorporates predictions in a black-box manner and outperforms any online algorithm if the accuracy is high, yet maintains provable guarantees if the prediction is very inaccurate. We provide both theoretical and experimental evidence to support our claims.


page 1

page 2

page 3

page 4


A New Approach to Capacity Scaling Augmented With Unreliable Machine Learning Predictions

Modern data centers suffer from immense power consumption. The erratic b...

Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds

We study the online problem of minimizing power consumption in systems w...

A Novel Prediction Setup for Online Speed-Scaling

Given the rapid rise in energy demand by data centers and computing syst...

Better and Simpler Learning-Augmented Online Caching

Lykouris and Vassilvitskii (ICML 2018) introduce a model of online cachi...

Robustification of Online Graph Exploration Methods

Exploring unknown environments is a fundamental task in many domains, e....

Customizing ML Predictions for Online Algorithms

A popular line of recent research incorporates ML advice in the design o...