This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book. 

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.

This book serves as a comprehensive introduction to the disruptive Fully Decoupled Radio Access Network (FD-RAN) architecture. A broad spectrum of the key enabling technologies are covered and organized into four themes: Physical Layer Transmission, Resource Management, Cost-efficient Operations, as well as Prototype and Potential Applications. It provides rethinking of network architecture and offers a unique solution to tackle the fundamental challenges and align with future demands of 6G.FD-RAN has emerged as a promising 6G architecture, with the following design objectives: decoupling, flexibility, simplicity, openness, intelligence, and cooperation. At its core, FD-RAN decomposes traditional base stations into three distinct functional entities: Control Signaling, Uplink Data Transmission, and Downlink Data Transmission. This decoupling enables flexible cooperation and allows independent optimization of uplink and downlink operations, leading to potential resource utilization improvement and network cost reduction.This book is particularly valuable for professionals, researchers, and stakeholders in academia, industry and standardization bodies, who are engaged in the development of next-generation mobile communication systems. This book presents novel concepts and solutions designed to support a more flexible, efficient, and forward-looking vision for 6G networks. Advanced-level students majoring in communications engineering/computer science can also find valuable content and inspiration from this book.

This book constitutes the refereed proceedings of the 4th International IFIP-TC6 Networking Conference, NETWORKING 2005, held in Waterloo, Canada in May 2005.The 105 revised full papers and 36 posters were carefully reviewed and selected from 430 submissions. The papers are organized in topical sections on peer-to-peer networks, Internet protocols, wireless security, network security, wireless performance, network service support, network modeling and simulation, wireless LAN, optical networks, Internet performance and Web applications, ad-hoc networks, adaptive networks, radio resource management, Internet routing, queuing models, monitoring, network management, sensor networks, overlay multicast, QoS, wirless scheduling, multicast traffic management and engineering, mobility management, bandwith management, DCMA, and wireless resource management.

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book. 

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.

This book serves as a comprehensive introduction to the disruptive Fully Decoupled Radio Access Network (FD-RAN) architecture. A broad spectrum of the key enabling technologies are covered and organized into four themes: Physical Layer Transmission, Resource Management, Cost-efficient Operations, as well as Prototype and Potential Applications. It provides rethinking of network architecture and offers a unique solution to tackle the fundamental challenges and align with future demands of 6G.FD-RAN has emerged as a promising 6G architecture, with the following design objectives: decoupling, flexibility, simplicity, openness, intelligence, and cooperation. At its core, FD-RAN decomposes traditional base stations into three distinct functional entities: Control Signaling, Uplink Data Transmission, and Downlink Data Transmission. This decoupling enables flexible cooperation and allows independent optimization of uplink and downlink operations, leading to potential resource utilization improvement and network cost reduction.This book is particularly valuable for professionals, researchers, and stakeholders in academia, industry and standardization bodies, who are engaged in the development of next-generation mobile communication systems. This book presents novel concepts and solutions designed to support a more flexible, efficient, and forward-looking vision for 6G networks. Advanced-level students majoring in communications engineering/computer science can also find valuable content and inspiration from this book.

This book constitutes the refereed proceedings of the 4th International IFIP-TC6 Networking Conference, NETWORKING 2005, held in Waterloo, Canada in May 2005.The 105 revised full papers and 36 posters were carefully reviewed and selected from 430 submissions. The papers are organized in topical sections on peer-to-peer networks, Internet protocols, wireless security, network security, wireless performance, network service support, network modeling and simulation, wireless LAN, optical networks, Internet performance and Web applications, ad-hoc networks, adaptive networks, radio resource management, Internet routing, queuing models, monitoring, network management, sensor networks, overlay multicast, QoS, wirless scheduling, multicast traffic management and engineering, mobility management, bandwith management, DCMA, and wireless resource management.