Many real-world services can be provided through multiple VNF graphs, corresponding, e.g., to high- and low-quality variants of the service itself. Based on this observation, we extend the concept of service scaling in network orchestration to service shifting, i.e., switching between the VNF graphs implementing the same service. Service shifting can serve multiple goals, from reducing operational costs to reacting to infrastructure problems. Furthermore, it enhances the flexibility of service-level agreements between network operators and third party content providers ("verticals"). In this paper, we introduce and describe the service shifting concept, its benefits, and the associated challenges, with special reference to how service shifting can be integrated within real-world 5G architectures and implementations. We conclude that existing network orchestration frameworks can be easily extended to support service shifting, and its adoption has the potential to make 5G network slices easier for the operators to manage under high-load conditions, while still meeting the verticals' requirements.
To adapt to continuously changing workloads in networks, components of the running network services may need to be replicated ( scaling the network service) and allocated to physical resources ( placement ) dynamically, also necessitating dynamic re-routing of flows between service components. In this paper, we propose joint optimization of scaling, placement, and routing (JASPER), a fully automated approach to jointly optimizing scaling, placement, and routing for complex network services, consisting of multiple (virtualized) components. JASPER handles multiple network services that share the same substrate network; services can be dynamically added or removed and dynamic workload changes are handled. Our approach lets service designers specify their services on a high level of abstraction using service templates . JASPER automatically makes scaling, placement and routing decisions, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability and effectiveness of the proposed approach. Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum. The next-generation mobile network anticipates integrated heterogeneous fronthaul and backhaul technologies referred to as a unified crosshaul architecture. The crosshaul enables a flexible and cost-efficient infrastructure for handling mobile data tsunami from dense Internet of things (IoT). However, stabilization, energy efficiency, and latency have not been jointly considered in the optimization of crosshaul performance. To overcome these issues, we propose an orchestration scheme referred to as the stabilized green crosshaul orchestration (SGCO). SGCO utilizes a Lyapunov-theory-based drift-plus-penalty policy to determine the optimal amount of offloaded data that should be processed either at the eastbound or westbound computing platforms to minimize energy consumption. To achieve system stability, the cache buffer is considered as the main constraint in developing the optimization process. Moreover, the amount of offloaded data transmitted via crosshaul links is selected by adopting the binary min-knapsack problem. Accordingly, a lightweight heuristic algorithm is proposed. As the cache buffer is stabilized and the computations are controlled, the SGCO ensures adjustable computing latency threshold for various IoT services. The performance analysis shows that the proposed SGCO scheme exposes effective energy consumption compared to other existing schemes while maintaining system stability considering latency.