Data-center network configurations are crucial in ensuring end-to-end differentiated service performance within 5G. Data-center networks encom-pass two domains: (i) the fat-tree networking fabric with leaf, spine and super-spine layers (ii) data-center server nodes with container and workload placement policies. These have traditionally been managed within silos with context and configurations driven within each domain. In this work, we examine the effect of configuration changes in one domain and its effect on the other. We develop Madelyn, a multi-domain multi-agent rein-forcement learning framework for data-center networks that can propose network-aware, virtual network function placement. This framework takes into account the data-center fabric wights, drop rates, capacities, load balancing and traffic shaping. It also considers the network function pod placements based on affinity / anti-affinity rules, node capacities and taints/tolerations. Using this multi-agent framework, we provide network aware scheduling policies for differentiated network function virtualization services running on Kubernetes pods within data-center networks. The results are demonstrated over a real traffic dataset collected over Ericsson's testbed networks.

5G Radio Access Network (RAN) slicing concerns strategies to share radio resources while guaranteeing differentiated service requirements. Current state of the art approaches make use of strict isolation or dedicated RAN physical resource block (PRB) partitioning among slices to ensure differentiated services. However, spectrum multiplexing may be rendered suboptimal due to isolation of resources; it further cannot handle variations in traffic patterns or intents in a dynamic way. In this paper, we propose a flexible multi-service partitioning strategy that can balance functional isolation and optimal sharing of resources. This system, called Muesli: Multi-objective Radio Resource Slice Management, makes use of model-based reinforcement learning techniques to dynamically modify PRB partitions. The reinforcement learning reward structure ensures that the system is trained to meet multiple objectives such as network slice Service Level Agreement (SLA) compliance, spectrum usage efficiency and fairness among customer classes. On a real use case from Ericsson, the throughput levels for individual services are shown to be optimized with accurate PRB partitioning.