Network slicing is a key enabler for 5G to support various applications. Slices requested by service providers (SPs) have heterogeneous quality of service (QoS) requirements, such as latency, throughput, and jitter. It is imperative that the 5G infrastructure provider (InP) allocates the right amount of resources depending on the slice's traffic, such that the specified QoS levels are maintained during the slice's lifetime while maximizing resource efficiency. However, there is a non-trivial relationship between the QoS and resource allocation. In this paper, this relationship is learned using a regression-based model. We also leverage a risk-constrained reinforcement learning agent that is trained offline using this model and domain randomization for dynamically scaling slice resources while maintaining the desired QoS level. Our novel approach reduces the effects of network modeling errors since it is model-free and does not require QoS metrics to be mathematically formulated in terms of traffic. In addition, it provides robustness against uncertain network conditions, generalizes to different real-world traffic patterns, and caters to various QoS metrics. The results show that the state-of-the-art approaches can lead to QoS degradation as high as 44.5% when tested on previously unseen traffic. On the other hand, our approach maintains the QoS degradation below a preset 10% threshold on such traffic, while minimizing the allocated resources. Additionally, we demonstrate that the proposed approach is robust against varying network conditions and inaccurate traffic predictions.

翻译：网络切片是5G支持多样化应用的关键使能技术。服务提供商（SP）请求的切片具有异构的服务质量（QoS）需求，例如时延、吞吐量和抖动。5G基础设施提供商（InP）必须根据切片流量分配适当数量的资源，以在切片生命周期内维持指定QoS等级的同时最大化资源效率。然而，QoS与资源分配之间存在复杂的非线性关系。本文采用基于回归的模型学习这一关系。我们进一步利用风险约束强化学习智能体，该智能体通过该模型与领域随机化进行离线训练，实现在维持所需QoS等级的同时动态扩展切片资源。由于该方法无需模型且不需要以流量函数形式数学化表述QoS指标，因此能够降低网络建模误差的影响。此外，该方法对不确定网络条件具有鲁棒性，可泛化至不同真实流量模式，并适配多种QoS指标。实验结果表明，在未预先训练的流量测试中，现有先进方法会导致高达44.5%的QoS退化。相比之下，本文方法在最小化资源分配的同时，可将此类流量的QoS退化维持在预设的10%阈值以下。我们还验证了所提方法在变化网络条件与不准确流量预测场景下的稳健性。