Towards Improving End-to-end Network Slices Control Through Reinforcement Learning

In the era of 5G networks, customer expectations in the realm of networking have surged significantly. They now demand highly personalized and tailor-made services that precisely align with their specific criteria for Quality of Service (QoS), Service Level Agreements (SLAs), and Key Performance Indicators (KPIs). Traditional 4G networks, which adhere to a “one-size-fits-all” model, are ill-suited to cope with these evolving demands. In response to this challenge, the concept of network slicing has emerged as a highly promising solution. Network slicing allows the partitioning of a single physical network into distinct isolated logical slices, each proficiently catering to the diverse needs of individual users. However, the dynamic nature of network traffic and computer networks introduces an additional layer of complexity to the implementation of network slicing. In this dissertation, we aim to enhance the strategy for network slice admission and allocation. Our primary approach is to evolve from the traditional static network slice model to an elastic network slice. This transition is designed to amplify system resource utilization efficiency and reduce costs for customers. As evidence of the utility of the elastic network slice, we present a use-case within a federated learning context. This illustrative case underscores the benefits and practical applicability of our proposed model. Furthermore, we delve into the intricacies of network slice provisioning within a multi-domain environment. This exploration encompasses both the strategies for partitioning and allocation.