Optimization of energy consumption in data centers using predictive analytics
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Motivation<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Energy Efficiency in Data Centers<br> 2.2 Predictive Analytics for Resource Management<br> 2.3 Machine Learning Models for Energy Consumption Prediction<br>3. Data Collection and Preprocessing<br> 3.1 Energy Usage Data<br> 3.2 Environmental Sensor Data<br> 3.3 Workload and Performance Metrics<br>4. Predictive Model Development<br> 4.1 Time Series Analysis and Forecasting<br> 4.2 Regression Models for Energy Prediction<br> 4.3 Anomaly Detection and Fault Diagnosis<br>5. Energy Management System Design<br> 5.1 Dynamic Resource Allocation Strategies<br> 5.2 Cooling System Optimization<br></p>
Thesis 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>Thesis Overview
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