Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning
This addresses the challenge of efficiently provisioning network slices for next-generation networks to meet low latency and high reliability demands, though it appears incremental as it applies an existing reinforcement learning method to a specific optimization problem.
The paper tackles the problem of real-time Network Slice Provisioning (NSP) with complex QoS requirements by modeling it as an online Multi-Objective Integer Programming Optimization (MOIPO) problem and solving it using Proximal Policy Optimization (PPO) with traffic demand prediction, achieving lower SLA violation rates and network operation costs compared to state-of-the-art MOIPO solvers.
Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet the low latency and high reliability of network applications. To this end, we model the real-time NSP as an Online Network Slice Provisioning (ONSP) problem. Specifically, we formulate the ONSP problem as an online Multi-Objective Integer Programming Optimization (MOIPO) problem. Then, we approximate the solution of the MOIPO problem by applying the Proximal Policy Optimization (PPO) method to the traffic demand prediction. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art MOIPO solvers with a lower SLA violation rate and network operation cost.