Graduate Project
 

Improving Peak Power Shaving in Data Centers using Deep Neural Networks

Público Deposited

Contenido Descargable

Descargar PDF
https://ir.library.oregonstate.edu/concern/graduate_projects/1v53k295k

Descriptions

Attribute NameValues
Creator
Abstract
  • Data centers have been charged a great amount of electric bill by the power company and demand charge can contribute up to 40% of the electric bill due to the "random" workload. This phenomenon can be avoided by using the existing Uninterrupted Power Supply (UPS) as the assistant power source to supply the servers. The UPS will shave the peak power by supporting the servers while the peak power exceeds threshold during the on-peak duration. For using the UPS to compensate the extra power, the problems are predicting the future data center resource usage and predicting the remaining capacity in the UPS battery. Since both problems involve in a highly correlated temporal relationship among their data, adopting Long-Short-Term-Memory(LSTM) Neural Network to perform the prediction solves the problems. After the experiment, the Long-Short-Term-Memory(LSTM) Neural Network solves the Resources Usage Prediction problem by having 6% Root Mean Square Error(RMSE) which outperforms the others and UPS Remaining Capacity achieves 7% RMSE.
License
Resource Type
Fecha de Emisión
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Declaración de derechos
Publisher
Peer Reviewed
Language

Relaciones

Parents:

This work has no parents.

Elementos

Miniatura Título Fecha de subida Visibilidad Acciones
file details FangQiMSPaper.pdf 2017-12-08 Público Descargar