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 an end-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.
