Learn to improve: A novel deep reinforcement learning approach for beyond 5G network slicing

Network slicing remains one of the key technologies in 5G and beyond 5G networks (B5G). By leveraging SDN and NVF techniques, it enables the coexistence of several heterogeneous virtual networks (VNs) on top of the same physical infrastructure. Despite the advantages it brings to network operators, network slicing raises a major challenge: Resource allocation of VNs, also known as the virtual network embedding problem (VNEP). VNEP is known to be an NP-Hard problem. Several heuristics, meta-heuristics and Deep Reinforcement Learning (DRL) based solutions were proposed in the literature to solve it. Regarding the first two categories, they can provide a solution for large scale problems within a reasonable time, but the solution is usually suboptimal, which leads to an inefficient utilization of the resources and increases the cost of the allocation process. For DRL-based approaches and due to the exploration-exploitation dilemma, the solution can be infeasible. To overcome these issues, we combine, in this work, deep reinforcement learning and relational graph convolutional neural networks in order to automatically learn how to improve the quality of VNEP heuristics. Simulation results show the effectiveness of our approach. Starting with an initial solution given by the heuristics our approach can find an amelioration, with an improvement in the order of 35%.

Automated summary

Network slicing is a key technology in 5G and B5G networks, allowing multiple virtual networks to coexist on the same physical infrastructure. However, resource allocation, known as VNEP, is a major challenge. Heuristics, meta-heuristics, and DRL-based solutions have been proposed, but they may result in suboptimal solutions and increased costs.