Offline SLA-Constrained Deep Learning for 5G Networks Reliable and Dynamic End-to-End Slicing

Abstract

In this paper, we address the issue of resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV)-based fifth generation (5G) networks. The different slices’ tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices’ traffics predictor based on a soft gated recurrent unit (GRU). We then build—at each virtual network function—joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely, violation rate -based SLA and resource bounds -based SLA. This is achieved by integrating dataset-dependent generalized non-convex constraints into the DNN offline optimization tasks that are solved via a non-zero sum two-player game strategy. In this respect, we highlight the role of the underlying hyperparameters in the trade-off between overprovisioning and slices’ isolation. Finally, using reliability theory, we provide a closed-form analysis for the lower bound of the so-called reliable convergence probability and showcase the effect of the violation rate on it.