Sustainable Cloud and Energy Services

This is the first book entirely devoted to providing a perspective on the state-of-the-art of cloud computing and energy services and the impact on designing sustainable systems. Cloud computing services provide an efficient approach for connecting infrastructures and can support sustainability in different ways. For example, the design of more efficient cloud services can contribute in reducing energy consumption and environmental impact. The chapters in this book address conceptual principles and illustrate the latest achievements and development updates concerning sustainable cloud and energy services. This book serves as a useful reference for advanced undergraduate students, graduate students and practitioners interested in the design, implementation and deployment of sustainable cloud based energy services. Professionals in the areas of power engineering, computer science, and environmental science and engineering will find value in the multidisciplinary approach to sustainable cloud and energy services presented in this book.

Provides a state-of-the-art look at sustainable cloud based energy systems Presents both well established and novel techniques for designing, developing and deploying sustainable systems Provides a set of sample projects in this area with realistic constraints

Autorentext
Dr. Wilson Rivera is a Professor of Computer Science and Engineering at the University of Puerto Rico at Mayaguez.

Inhalt
Chapter 1.Cloud Computing for Internet of Things: Architecture, Issues and Challenges in integration.- Chapter 2.A self-governing and decentralized network of smart objects to share electrical power autonomously.- Chapter 3.Implementing Energy Service Automation using Cloud Technologies and Public Communications Networks.- Chapter 4.Privacy-Preserving Smart Grid Tariff Decisions with Block-Chain-Based Smart Contracts.- Chapter 5.Energy Cloud: Services for Smart Buildings.- Chapter 6.Virtual Machine Consolidation Algorithms for Energy-Efficient Dynamic Cloud Resource Management: A Review.- Chapter 7.Energy Saving in Cloud by using Enhanced Instance Based Learning (EIBL) for Resource Prediction.- Chapter 8.Short-Term Prediction Model to Maximize Renewable Energy Usage in Cloud Data Centers.- Chapter 9.Optimal Sizing of a Micro-Hydrokinetic Pumped-Hydro-Storage Hybrid System for Different Demand Sectors.- Chapter 10.Impact of different South African demand sectors on grid-connected PV systems' optimal energy dispatch under Time of Use Tariff.