Online based learning for predictive end-to-end network slicing in 5G networks

5G networks are expected to provide a variety of services over the same physical infrastructure equipped with a combination of radio and wired transport networks and leveraging network virtualization and Software Defined Networking (SDN). In particular, network slicing is envisioned as a promising solution to enable optimal support for heterogeneous services sharing the same infrastructure. To this end, we design and implement, in this paper, an SDN based architecture for end-to-end network slicing which proactively and dynamically adapts radio slices to the transport network slices. The developed architecture enables the creation, modification and continuity of radio and transport network slices while considering their resource and Quality-of-Service (QoS) requirements. It leverages Machine Learning for predicting radio slices capacities, improving network resource utilization, and predicting congestion within each network slice. We also formulate this network slicing problem as a Linear Program (LP) aiming to minimize the total network delay. Finally, we propose an efficient heuristic algorithm with low time complexity and high estimation accuracy to solve large problem instances. Experimental results using the OpenAirInterface (OAI) platform, FlexRAN, ONOS SDN Controllers and OpenvSwitch demonstrate the efficiency of our approach in terms of guaranteeing low latency and high network throughput.