Summary Network “softwarization” is enabling new features such as detecting the risk to violate a Service Level Agreement (SLA) and performing the required automated control actions on a network slice. A real‐time Network Function Virtualization (NFV) Management and Orchestration (MANO) system should detect any SLA degradation and act to recover the SLA. The closed‐loop automation approach monitors the state of the network and triggers policy‐based control actions when needed. The delay between the detection of an SLA issue and the completion of a correcting action is critical for satisfying latency‐related SLAs. Thus, in this article we propose this delay as a new key performance indicator (KPI) for NFV MANO systems, which we call Closed‐Loop Orchestration Delay (CLOD). CLOD is subdivided into independent time intervals that depend on the NFV MANO platform, the target Virtualized Infrastructure Management (VIM) and the target virtual network function (VNF) software. CLOD is evaluated on a real use case: a latency‐sensitive elastic network slice in which Internet user round‐trip time (RTT) is monitored. When the service level of the slice drops below a predefined limit a new data plane is deployed near the user reducing latency. In the evaluation, we considered two approaches: (i) a general implementation of the NFV MANO architecture based on the Open Network Automation Platform (ONAP) and OpenStack and (ii) an optimized ad‐hoc baseline implementation to minimize CLOD. We provide the details of both implementations and compare their CLODs leading to useful insights for the NFV MANO community.

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.