Screen LibraryComputational intelligence for green cloud computing and digital waste management /
"In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warmin...
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| Corporate Authors: | |
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| Group Author: | ; ; ; |
| Published: |
IGI Global, Engineering Science Reference,
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| Publisher Address: | Hershey, PA : |
| Publication Dates: | [2024] |
| Literature type: | Book |
| Language: | English |
| Series: |
Advances in computational intelligence and robotics (ACIR) book series,
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| Subjects: | |
| Summary: |
"In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption.Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource. "-- |
| Item Description: | "Premier reference source." |
| Carrier Form: | xxi, 405 pages : illustrations ; 29 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9798369315521 |
| Index Number: | HC79 |
| CLC: | TP3-05 |
| Call Number: | TP3-05/C738-7 |
| Contents: | Chapter 1. Machine learning and deep learning algorithms for green computing -- Chapter 2. Green computing and the quest for sustainable solutions -- Chapter 3. Navigating green computing challenges and strategies for sustainable solutions -- Chapter 4. Impact of data centers on power consumption, climate change, and sustainability -- Chapter 5. Power-aware virtualization: dynamic voltage frequency scaling insights and communication-aware request stacking -- Chapter 6. Strategies to achieve carbon neutrality and foster sustainability in data centers -- Chapter 7. Computational intelligence for green cloud computing and digital waste management: intelligent computing resource management in cloud/fog/edge distributed computing -- Chapter 8. Modern technological innovation in digital wase management -- Chapter 9. Achieving green sustainability in computing devices in machine learning and deep learning techniques -- Chapter 10. Future trends and significant solutions for intelligent computing resource management -- Chapter 11. Green computing-based digital waste management and resource allocation for distributed fog data centers -- Chapter 12. Sustainable waste management OOA-enhanced mobilenetV2-TC model for trash image classification -- Chapter 13. Computational intelligence for green cloud computing and digital waste management -- Chapter 14. Efficient resource management in green computing based on ISHOA task scheduling with secure chacha20-poly1305 authenticated encryption-based data transmission -- Chapter 15. Dual-CNN-based waste classification system using IoT and HDS algorithm -- Chapter 16. Intelligent healthcare provisioning in fog using grey wolf optimization -- Chapter 17. Fog computing-based framework and solutions for intelligent systems: enabling autonomy in vehicles. |