Security-aware 5G RAN slice mapping with tiered isolation in physical-layer secured metro-aggregation elastic optical networks using heuristic-assisted DRL

Abstract

The optical transport network (OTN) encryption technology is attractive to solve the physical-layer security in services for the light-path provision process. This paper mainly explores the security-aware 5G radio access network (RAN) slice mapping problem with the tiered isolation (TI) policy, which decides the solution for aggregating service into the physical-layer secured metro-aggregation elastic optical networks (MA-EONs). We first introduce the physical-layer secured OTNs and illustrate their differences from the traditional optical networks. Then, we formulate the 5G RAN slice mapping problem in physical-layer secured MA-EONs as an exact integer linear programming (ILP) model to minimize the average cost (AC), which consists of the number of utilized processing pools (PPs)/general-purpose processors (GPPs)/virtual machines (VMs), and maximum frequency slot index (MFSI) on the light-paths, meanwhile satisfying the given slice’s latency, isolation, and security requirements. After that, to overcome the non-scalability problem of the ILP model, a heuristic-assisted deep reinforcement learning (HA-DRL) algorithm is proposed to obtain a near-optimal solution for large-scale network scenarios, where the classical shortest path algorithm is employed in the DRL to shrink the size of the exploration space and accelerate the convergence process. Finally, we evaluate the proposed ILP model and HA-DRL algorithm through extensive simulations. Simulation results indicate that our proposed HA-DRL method can find approximate solutions to the ILP model in the small-scale network scenario. Furthermore, the HA-DRL method can also achieve higher resource efficiency compared with benchmark heuristic first-fit algorithms in the large-scale network scenario. In comparison to the first-fit algorithm benchmark, the proposed HA-DRL can achieve up to 9.4% AC reduction in large-scale network scenarios.