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.
翻译:网络切除(NS)对于有效促进下一代网络的不同网络应用至关重要,然而,复杂的服务质量要求(QOS)和网络服务的多样性要求需要大量计算时间来优化网络切片提供(NSP),遗留的优化方法对于满足网络应用的低延迟性和高可靠性具有挑战性。为此,我们将实时网络切片提供(ONSP)模拟成在线网络切片提供(ONSP)问题。具体地说,我们将ONSP问题设计成一个在线多目标性综合规划优化(MOIPO)问题。然后,我们通过对流量需求预测采用Proximal政策优化(PPPO)方法,大致解决MOIPO问题。我们的模拟结果显示,与最先进的MOIPO解决方案相比,拟议方法的有效性,其违反率和网络运行成本较低。