Learning Augmented Energy Minimization via Speed Scaling

2020-10-22 · NeurIPS 2020 12 · Étienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson

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.

📄 PDF Abstract BibTeX arXiv:2010.11629

Code (1)

andreasr27/LAS 공식 구현

Tasks

BIG-bench Machine Learning Management

Energy-Efficient Scheduling with Predictions

2024-02-27 · NeurIPS 2023 11 · Eric Balkanski, Noemie Perivier, Clifford Stein, Hao-Ting Wei

An important goal of modern scheduling systems is to efficiently manage power usage. In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs with the dual objective o…

Prediction Scheduling

A Novel Prediction Setup for Online Speed-Scaling

2021-12-06 · Antonios Antoniadis, Peyman Jabbarzade Ganje, Golnoosh Shahkarami

Given the rapid rise in energy demand by data centers and computing systems in general, it is fundamental to incorporate energy considerations when designing (scheduling) algorithms. Machine learning can be a useful appr…

BIG-bench Machine Learning Prediction Scheduling

Continuous Inference in Graphical Models with Polynomial Energies

2013-06-01 · CVPR 2013 6 · Mathieu Salzmann

In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, where …

Diversity

Extended Dynamic Programming and Fast Multidimensional Search Algorithm for Energy Minization in Stereo and Motion

2014-10-29 · Mikhail G. Mozerov

This paper presents a novel extended dynamic programming approach for energy minimization (EDP) to solve the correspondence problem for stereo and motion. A significant speedup is achieved using a recursive minimum searc…

GPU

Reinforcement Learning with Subspaces using Free Energy Paradigm

2020-12-13 · Milad Ghorbani, Reshad Hosseini, Seyed Pooya Shariatpanahi, Majid Nili Ahmadabadi

In large-scale problems, standard reinforcement learning algorithms suffer from slow learning speed. In this paper, we follow the framework of using subspaces to tackle this problem. We propose a free-energy minimization…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Thompson Sampling