p> Cloudified mobile networks, such as 5G, are expected to deliver a multitude of services to several slices in parallel, while having reduced capital and operating expenses. The 5G mobile systems, therefore, need to ensure that the SLAs of customized end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualized network
... [Show full abstract] components and interfaces of its cloud mobile network, as well as tracking the performance at its radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a distributed telemetry framework in which UEs act as early warning sensors. Upon flagging an anomaly, the cloudified mobile network activates a machine learning model to attribute the cause of the anomaly. We employ active, passive and in-band telemetry in our monitoring framework
and achieve an impressive performance of 85% F1 score in detecting anomalies caused by different bottlenecks, and an overall 89% F1 score in attributing these bottlenecks. Our distributed framework achieves almost same bottleneck attribution accuracy to that of acentralized monitoring system but with no overhead of transmitting UE-based telemetry data to the centralized controller.
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