Choong Seon Hong's Books

A Survey on Coordinated Power Management in Multi-Tenant Data Centers

By: Thant Zin Oo,Nguyen H. Tran,Shaolei Ren,Choong Seon Hong

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A Survey on Coordinated Power Management in Multi-Tenant Data Centers

By: Thant Zin Oo,Nguyen H. Tran,Shaolei Ren,Choong Seon Hong

This book investigates the coordinated power management of multi-tenant data centers that account for a large portion of the data center industry. The authors include discussion of their quick growth and their electricity consumption, which has huge economic and environmental impacts. This book covers the various coordinated management solutions in the existing literature focusing on efficiency, sustainability, and demand response aspects. First, the authors provide a background on the multi-tenant data center covering the stake holders, components, power infrastructure, and energy usage. Then, each power management mechanism is described in terms of motivation, problem formulation, challenges and solution.

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Page Count

176

Publisher

Springer

Published Date

ISBN 10

3319660624

ISBN 13

9783319660622

Network Slicing for 5G and Beyond Networks

By: S. M. Ahsan Kazmi,Latif U. Khan,Nguyen H. Tran,Choong Seon Hong

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Network Slicing for 5G and Beyond Networks

By: S. M. Ahsan Kazmi,Latif U. Khan,Nguyen H. Tran,Choong Seon Hong

This book provides a comprehensive guide to the emerging field of network slicing and its importance to bringing novel 5G applications into fruition. The authors discuss the current trends, novel enabling technologies, and current challenges imposed on the cellular networks. Resource management aspects of network slicing are also discussed by summarizing and comparing traditional game theoretic and optimization based solutions. Finally, the book presents some use cases of network slicing and applications for vertical industries. Topics include 5G deliverables, Radio Access Network (RAN) resources, and Core Network (CN) resources. Discusses the 5G network requirements and the challenges therein and how network slicing offers a solution Features the enabling technologies of future networks and how network slicing will play a role Presents the role of machine learning and data analytics for future cellular networks along with summarizing the machine learning approaches for 5G and beyond networks

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Page Count

153

Publisher

Springer

Published Date

ISBN 10

3030161706

ISBN 13

9783030161705

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

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Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

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Page Count

257

Publisher

Springer Nature

Published Date

ISBN 10

9811649634

ISBN 13

9789811649639

Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

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Federated Learning for Wireless Networks

By: Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han

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Solve jigsaw puzzle

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Page Count

257

Publisher

Springer Nature

Published Date

ISBN 10

9811649634

ISBN 13

9789811649639

Network Slicing for 5G and Beyond Networks

By: S. M. Ahsan Kazmi,Latif U. Khan,Nguyen H. Tran,Choong Seon Hong

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Network Slicing for 5G and Beyond Networks

By: S. M. Ahsan Kazmi,Latif U. Khan,Nguyen H. Tran,Choong Seon Hong

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N/A

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Solve jigsaw puzzle

Age

Page Count

153

Publisher

Springer

Published Date

ISBN 10

3030161706

ISBN 13

9783030161705

Book's by Choong Seon Hong

A Survey on Coordinated Power Management in Multi-Tenant Data Centers

A Survey on Coordinated Power Management in Multi-Tenant Data Centers

Network Slicing for 5G and Beyond Networks

Network Slicing for 5G and Beyond Networks

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

Federated Learning for Wireless Networks

Network Slicing for 5G and Beyond Networks

Network Slicing for 5G and Beyond Networks

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