Multi-access Edge Computing (MEC) technologies bring important improvements in terms of network bandwidth, latency and use of context information, critical for services like multimedia streaming, augmented and virtual reality. In future deployments, operators will need to decide how many MEC Points of Presence (PoPs) are needed and where to deploy them, also considering the number of base stations needed to support the expected traffic. This article presents an application of inhomogeneous Poisson point processes with hard-core repulsion to model feasible MEC infrastructure deployments. With the presented methodology a mobile network operator knows where to locate the MEC PoPs and associated base stations to support a given set of services. We evaluate our model with simulations in realistic scenarios, namely Madrid city center, an industrial area, and a rural area.

Industry 4.0 aims at supporting smarter and autonomous processes while improving agility, cost efficiency and user experience. To fulfill its promises, properly processing the data of the industrial processes and infrastructures is required. Artificial Intelligence (AI) appears as a strong candidate to handle all generated data, and to help in the automation and smartification process. This article overviews the Digital Twin as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the Digital Twin such as Edge, Fog and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. Finally, movement prediction is selected and experimentally validated using real data generated by a Digital Twin for robotic arms with results showcasing its potential.

Ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the automation and optimization of the software-defined, virtualization-based next generation of networks such as 5G and beyond. Network services provided for autonomous vehicles, factory automation, e-health and cloud robotics often require strict delay bounds and reliability constraints influenced by the location of its composing Virtual Network Functions. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. Moreover, these devices are continuously on the move and might lose network connection or run out of battery, which further challenge service delivery in this dynamic environment. This work tackles the mobility, and battery restrictions; as well as the temporal aspects and conflicting traits of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a costminimizing Virtual Network Function placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios.

Networks can now process data as well as transporting it; it follows that they can support multiple services, each requiring different key performance indicators (KPIs). Because of the former, it is critical to efficiently allocate network and computing resources to provide the required services, and, because of the latter, such decisions must jointly consider all KPIs targeted by a service. Accounting for newly introduced KPIs (e.g., availability and reliability) requires tailored models and solution strategies, and has been conspicuously neglected by existing works, which are instead built around traditional metrics like throughput and latency. We fill this gap by presenting a novel methodology and resource allocation scheme, named OKpi, which enables high-quality selection of radio points of access as well as VNF (Virtual Network Function) placement and data routing, with polynomial computational complexity. OKpi accounts for all relevant KPIs required by each service, and for any available resource from the fog to the cloud. We prove several important properties of OKpi and evaluate its performance in two realworld scenarios, finding it to closely match the optimum.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.