This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Network slicing is enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. For a network slice with a target traffic load, the E2E service delivery is enabled by virtual network function (VNF) placement and traffic routing with static resource allocations. When data traffic enters the network, the traffic load is dynamic and can deviate from the target value, potentially leading to QoS performance degradation and network congestion. Data traffic has dynamics in different time granularities. For example, the traffic statistics (e.g., mean and variance) can be non-stationary and experience significant changes in a coarse time granularity, which are usually predictable. Within a long time duration with stationary traffic statistics, there are traffic dynamics in small timescales, which are usually highly bursty and unpredictable. To provide continuous QoS performance guarantee and ensure efficient and fair operation of the network slices over time, it is essential to develop dynamic resource management schemes for the embedded services in the presence of traffic dynamics during virtual network operation. Queueing theory is used in system modeling, and different techniques including optimization and machine learning are applied to solving the dynamic resource management problems. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

This is the first comprehensive book about the emerging technology of Network-Centric Service-Oriented Enterprise (NCSOE). It establishes a system-of-systems (SoS) view of information technologies. The book discusses the practical capability of a competitive ecosystem in terms of how to achieve decision superiority from exploiting information and situation awareness as a key enabler in multiple sectors of the economy.

This book provides a comprehensive investigation on new technologies for future vehicular networks. The authors propose different schemes to efficiently manage the multi-dimensional resources for supporting diversified applications. The authors answer the questions of why connected and automated vehicle technology should be considered; how the multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) technologies can be helpful to vehicular networks; how to efficiently manage the multi-dimensional resources to support different vehicular applications with guaranteed quality-of-service (QoS) requirements; and how to adopt optimization and AI technologies to achieve resource management in vehicular networks. The book is pertinent to researchers, professionals, academics and students in vehicular technologies.

This book provides a timely and comprehensive study of developing efficient network slicing frameworks in both 5G wireless and core networks. It also presents protocol stack layer perspectives, which includes virtual network topology design, end-to-end delay modeling, dynamic resource slicing, and link-layer and transport-layer protocol customization. This book provides basic principles, concepts and technologies for communication, computing and networking. Optimization and queueing analysis techniques are applied to solving different problems for network slicing illustrated in this book as well. Researchers working in the area of network slicing in 5G networks and beyond, and advanced-level students majoring in electrical engineering, computer engineering and computer science will find this book useful as a reference or secondary textbook. Professionals in industry seeking solutions to resource management for 5G networks and beyond will also want to purchase this book.

This book is based on the “Summer Simulation Multi-Conference” (SCSC), which has been a prominent platform for the dissemination of scholarly research in the M&S community for the last 50 years. In keeping with the conference’s seasonal title, the authors have called this half-century “the summer of simulation,” and it has led not only to simulation-based disciplines but also simulation as a discipline. This book discusses contributions from the SCSC in four sections. The first section is an introduction to the work. The second section is devoted to contributions from simulation research fellows who were associated with the SCSC, while the third section features the SCSC’s most influential contributions. Lastly, the fourth section includes contributions from the best papers in the last five years. Features: • A comprehensive volume dedicated to one of the simulation domain’s major conferences: the SCSC • Offers a scientometric analysis of the SCSC • Revisits high-impact topics from 50 years of the SCSC • Includes chapters by simulation research fellows associated with the SCSC • Presents updated best-paper contributions from the recent conference This work will be of value to anyone interested in the evolution of modeling and simulation over the last fifty years. Readers will gain a perspective on what drove this evolution, and develop an understanding of the key contributions that allowed this technology to grow into its own academic discipline and profession.

Resource management has a long history in computing, from the early days of time-shared machines with pioneering fundamental work on run-time systems, distributed systems, real-time operating systems and middleware. The longevity and fundamental importance of the topic has resulted in an incredibly large body of work for on-chip resource management in the past two decades. The possible combinations of sub-topics and variations in the assumptions, use of different terminology, metrics, goals, and use-cases, leaves anyone attempting to review the literature overwhelmed. On-Chip Dynamic Resource Management is the first comprehensive and coherent review of all aspects of on-chip run-time resource management designed to facilitate understanding of recent trends in dynamic and adaptive strategies. The authors provide the reader with a framework within which they can navigate both existing, as well as evolving research efforts in on-chip dynamic resource management. Written by leading experts in the field, researchers and students are provided a structured review and discussion of the state of the art that is divided along the primary objectives of resource management techniques: performance, power, reliability and quality of service.