Forecasting power-efficiency related key performance indicators for modern data centers using LSTMs

TL;DR: In this paper , the authors provide a better grasp of energy efficiency evaluation and encourage researchers to combine theory and practice in energy-efficiency evaluation and utilize more advanced metrics to assess data center energy efficiency.

TL;DR: This study reviews past and present efforts that use ML techniques in heat transfer from the fundamental level to full-scale applications, including the use of ML to build reduced-order models, predict heat transfer coefficients and pressure drop, real-time analysis of complex experimental data, and optimize large-scale thermal systems in a variety of applications.

TL;DR: The proposed LSTM with tuned-PSO and Bifold-Attention mechanism’s accuracy is adaptable in daily, weekly, and monthly multivariate time-series datasets, and has lower MAPE and RMSE than baselines in this study.

TL;DR: In this paper , the authors analyzed the electricity saving potential related to video streaming in Europe from 2020 to 2030, considering three trend scenarios (Business-as-usual, Gray, and Green) and modeled bottom-up, taking specific electricity consumption of data transmission networks, end-use devices, and data centers into account.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: It is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution.

TL;DR: This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.