商品描述
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.
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.
商品描述(中文翻譯)
本書提供了一個及時且全面的研究，探討服務導向的第五代（5G）及更高版本核心網絡中的動態資源管理。這包括開發高效的計算資源供應和排程解決方案，以保證端到端（E2E）數據傳遞延遲的一致服務性能。
網絡切片是由軟件定義網絡（SDN）和網絡功能虛擬化（NFV）範式實現的。對於具有目標流量負載的網絡切片，通過虛擬網絡功能（VNF）的部署和流量路由以及靜態資源分配實現端到端服務交付。當數據流量進入網絡時，流量負載是動態的，可能偏離目標值，可能導致QoS性能下降和網絡擁塞。數據流量在不同的時間粒度上具有動態性。例如，流量統計數據（例如平均值和方差）可能是非穩態的，在粗粒度的時間尺度上會發生顯著變化，通常是可預測的。在具有穩態流量統計數據的長時間段內，存在小時間尺度上的流量動態，通常是高爆發性和不可預測的。為了在虛擬網絡運營期間的流量動態存在下，提供持續的QoS性能保證並確保網絡切片的高效和公平運作，必須開發動態資源管理方案。系統建模使用了排隊理論，並應用了優化和機器學習等不同技術來解決動態資源管理問題。
基於簡化的M/M/1排隊模型和泊松流量到達，提出了一個流量遷移的優化模型，以應對平均流量變化的大時間尺度，同時解決負載平衡和流量遷移開銷之間的平衡。為了克服泊松流量模型的局限性，作者提出了一種機器學習方法，用於動態VNF資源調整和遷移。這種新的解決方案捕捉了真實世界流量跟踪中的固有流量模式，預測VNF資源調整的資源需求，並觸發自適應的VNF遷移決策，以實現長期的負載平衡、遷移成本降低和資源過載懲罰抑制。研究人員和在電氣工程、計算工程和計算機科學領域工作的研究生將會發現本書作為參考書或輔助教材非常有用。同時，業界專業人士尋求解決5G及更高版本網絡的動態資源管理問題也會希望購買本書。
