Optimization of energy consumption in data centers using predictive analytics
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Background<br>
- 1.2Motivation<br>
- 1.3Objectives<br>
- 2.Literature Review<br>
- 2.1Energy Efficiency in Data Centers<br>
- 2.2Predictive Analytics for Resource Management<br>
- 2.3Machine Learning Models for Energy Consumption Prediction<br>
- 3.Data Collection and Preprocessing<br>
- 3.1Energy Usage Data<br>
- 3.2Environmental Sensor Data<br>
- 3.3Workload and Performance Metrics<br>
- 4.Predictive Model Development<br>
- 4.1Time Series Analysis and Forecasting<br>
- 4.2Regression Models for Energy Prediction<br>
- 4.3Anomaly Detection and Fault Diagnosis<br>
- 5.Energy Management System Design<br>
- 5.1Dynamic Resource Allocation Strategies<br>
- 5.2Cooling System Optimization<br></p>
Project Abstract
<p> This project aims to optimize energy consumption in data centers through the application of predictive analytics and machine learning techniques. By analyzing historical energy usage data, environmental factors, and workload patterns, the project seeks to develop predictive models for dynamic resource allocation and cooling system optimization. The project will involve data collection, model training, and the implementation of an energy management system for real-time decision-making. <br></p>Project Overview
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