@article{17265,
  keywords = {congestion, classification, Telemetry, Bottleneck, Mobile Cloud Network, Anomaly},
  author = {Mah-rukh Fida and Azza Ahmed and Thomas Dreibholz and Andr{\'e}s Ocampo and Ahmed Elmokashfi and Foivos Michelinakis},
  title = {Bottleneck Identification in Cloudified Mobile Networks based on Distributed Telemetry},
  abstract = {Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85\% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89\% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system.},
  year = {2023},
  journal = {Transactions on Mobile Computing},
  pages = {1{\textendash}18},
  publisher = {IEEE},
  issn = {1558-0660},
  url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=10239332},
  doi = {10.1109/TMC.2023.3312051},
}