Use cases for network determinism may involve the traversal of multiple network domains. A typical example is the edge-cloud virtualization of the controllers for industrial automation, where the end-to-end data path may include a private mobile network and a wired local-area network within the factory premises, a public transport network between the factory and the edge-cloud data center, and a switching fabric within the same data center. Maintaining deterministic guarantees throughout these domains, likely operated by different providers, is a daunting task when each domain relies on its own control and data-plane technologies for satisfaction of its allocated portion of the end-to-end requirements. We propose a universal model for determinism in wired packet networks that simplifies the realization of deterministic services over chains of network domains. The model builds on a novel traffic shaper where routing information constrains the packet scheduling decisions. The shaper, and therefore the provisioning of resources for every deterministic service, is only needed at the ingress edge of each wired domain. We conjecture perdomain latency bounds enforced by the shaper and corroborate them with experimental evidence. The bounds are easy to combine for multi-domain network paths.

This paper explores a deep reinforcement learning approach applied to the packet routing problem with high-dimensional constraints instigated by dynamic and autonomous communication networks. Our approach is motivated by the fact that centralized path calculation approaches are often not scalable, whereas the distributed approaches with locally acting nodes are not fully aware of the end-to-end performance. We instead hierarchically distribute the path calculation over designated nodes in the network while taking into account the end-to-end performance. Specifically, we develop a hierarchical cluster-oriented adaptive per-flow path calculation mechanism by leveraging the Deep Double Q-network (DDQN) algorithm, where the end-to-end paths are calculated by the source nodes with the assistance of cluster (group) leaders at different hierarchical levels. In our approach, a deferred composite reward is designed to capture the end-to-end performance through a feedback signal from the source node to the group leaders and captures the local network performance through the local resource assessments by the group leaders. Our approach is expected to scale in large networks, adapt to the dynamic demand, utilize the network resources efficiently and can be applied to segment routing.

The next-generation 5G mobile network architecture will support the rapid deployment of new, dynamic network services that are capable of responding to current network conditions and demands. Software-defined Networking (SDN), virtualization technologies, and real-time analytics are the core components that will enable an adaptive and responsive 5G network. We present a case study of a real-time communications (RTC) video service that highlights the manner in which the core components (SDN, virtualization, analytics) allow a flexible and elastic 5G network. Because an end-to-end 5G network does not exist today, we construct one using artifacts from the current 4G/LTE network to host our dynamic network enabled RTC service. We identify three main insights from executing our service that could prove beneficial to the 5G network evolution: need for efficient horizontal control, need to limit identifier proliferation, and the existence of control-plane network functions in service network-function graphs.