Authors :
Rufaro Sydney Madebwe; Tinashe Butsa
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3YB1O4u
DOI :
https://doi.org/10.5281/zenodo.7645245
Abstract :
Data centres have become a major part of
computing, and with the advent of cloud computing their
demand has soared. The increase in demand for cloud
services has led to a data centre boom, however, the data
centres tend to consume large amounts of power. The
advent of Green computing has led to various researches
into how to make computing on a large scale more
sustainable. This has led to the evolution of power
consumption prediction researches that are meant to help
ease the use of power by data centres. In this regard, this
research aims to look at ways to cope with the power
consumption through adoption of Deep Learning to assist
with feature selection. This method aims to look beyond
the prior researches into power consumption which only
looked at certain factors mainly consumption by the server
and not the whole data centre. Key to this whole research
area are the following phases: (i) performance monitoring
and energy-related feature acquisition; (ii) essential
feature selection; and (iii) model establishment and
optimization.
Keywords :
Green computing, Deep Learning, Feature Selection
Data centres have become a major part of
computing, and with the advent of cloud computing their
demand has soared. The increase in demand for cloud
services has led to a data centre boom, however, the data
centres tend to consume large amounts of power. The
advent of Green computing has led to various researches
into how to make computing on a large scale more
sustainable. This has led to the evolution of power
consumption prediction researches that are meant to help
ease the use of power by data centres. In this regard, this
research aims to look at ways to cope with the power
consumption through adoption of Deep Learning to assist
with feature selection. This method aims to look beyond
the prior researches into power consumption which only
looked at certain factors mainly consumption by the server
and not the whole data centre. Key to this whole research
area are the following phases: (i) performance monitoring
and energy-related feature acquisition; (ii) essential
feature selection; and (iii) model establishment and
optimization.
Keywords :
Green computing, Deep Learning, Feature Selection
