Design of Automatic Greener Healthy System For Data Center using Deep Learning

The rapid expansion of data center resources, driven by the increasing demand for a variety of customer services, has led to higher energy consumption and the emission of heat and harmful gases, contributing to global warming. To tackle this pressing issue, the Greener Healthy System (GHS) has been...

Full description

Saved in:

Bibliographic Details
Published in:	2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 6
Main Authors:	Dhanalakshmi, M., Vyshali Rao, K P, Harish, L, Vinay Raj, A.S., Rath, Subhashree
Format:	Conference Proceeding
Language:	English
Published:	IEEE 24-06-2024
Subjects:	Booster Data centers Energy consumption Gases Global warming Green products Greener Healthy System(GHS) Internet of Things (IoT) Monitoring Predictive models Pressing Reliability Sensors
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The rapid expansion of data center resources, driven by the increasing demand for a variety of customer services, has led to higher energy consumption and the emission of heat and harmful gases, contributing to global warming. To tackle this pressing issue, the Greener Healthy System (GHS) has been developed to reduce the environmental impact of data center operations. GHS collects data from IoT sensors that monitor various harmful emissions within the data center. This data is then analyzed using machine learning algorithms to ensure high accuracy and reliability. Based on the analysis, the system autonomously implements energy-efficient strategies and optimizes cooling mechanisms, which helps to lower energy consumption. As a result, the release of harmful gases is reduced, mitigating the effects of global warming. Additionally, GHS utilizes machine learning techniques to refine predictive models for identifying and reducing harmful emissions within the data center, promoting a more sustainable and environmentally friendly operation.
ISSN:	2473-7674
DOI:	10.1109/ICCCNT61001.2024.10724153