论文标题
使用深厚的加固学习对网络切片进行多目标配置
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning
论文作者
论文摘要
网络切片（NS）对于有效启用下一代网络中的发散网络应用至关重要。尽管如此，网络服务中的复杂服务质量（QoS）要求和多样的异质性需要网络切片供应（NSP）优化的高计算时间。传统优化方法在满足网络应用程序的低潜伏期和高可靠性方面具有挑战性。为此，我们将实时NSP建模为在线网络切片配置（ONSP）问题。具体来说，我们将ONSP问题作为在线多目标整数编程优化（MOIPO）问题。然后，我们通过将近端策略优化（PPO）方法应用于交通需求预测来近似于Moipo问题的解决方案。我们的仿真结果表明，与最先进的Moipo求解器相比，该方法的有效性具有较低的SLA违规率和网络操作成本。
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.
