Automated decision making algorithms are expected to play a key role in management and orchestration of network slices in 5G and beyond networks. State-of-the-art algorithms for automated orchestration and management tend to rely on data-driven methods which require a timely and accurate view of the network. Accurately monitoring an end-to-end (E2E) network slice requires a scalable monitoring architecture that facilitates collection and correlation of data from various network segments comprising the slice. The state-of-the-art on 5G monitoring mostly focuses on scalability, falling short in providing explicit support for network slicing and computing network slice key performance indicators (KPIs). To fill this gap, in this paper, we present MonArch, a scalable monitoring architecture for 5G, which focuses on network slice monitoring, slice KPI computation, and an application programming interface (API) for specifying slice monitoring requests. We validate the proposed architecture by implementing MonArch on a 5G testbed, and demonstrate its capability to compute a network slice KPI (e.g., slice throughput). Our evaluations show that MonArch does not significantly increase data ingestion time when scaling the number of slices and that a 5-second monitoring interval offers a good balance between monitoring overhead and accuracy.

翻译：自动化决策算法预计将在5G及未来网络的网络切片管理与编排中发挥关键作用。当前最先进的自动化编排与管理算法往往依赖于数据驱动的方法，这要求网络能够提供及时且准确的视图。精确监控端到端网络切片需要一个可扩展的监控架构，以便于从构成切片的各个网段中收集和关联数据。当前5G监控技术主要集中在可扩展性上，但在明确支持网络切片以及计算网络切片关键性能指标方面存在不足。为填补这一空白，本文提出了MonArch——一种面向5G的可扩展监控架构，专注于网络切片监控、切片KPI计算，以及用于指定切片监控请求的应用编程接口。我们通过在5G测试床上实现MonArch来验证该架构，并展示其计算网络切片KPI（例如切片吞吐量）的能力。评估结果表明，MonArch在扩展切片数量时不会显著增加数据摄入时间，且5秒的监控间隔能在监控开销与准确性之间取得良好平衡。