As a Google Cloud Platform certified architect I really should blog some more about my actual usage of GCP. One of my favourite tools is Dataproc as it provides a managed Spark & Hadoop environment and enables a lambda architecture suitable for complex network event processing and function remediation.
A mobile radio network is a dynamical system that can be modelled ergodically. Meaning that the radio network performance in geometrical space should be observed and modelled over a period of time. Storing this sort of data requires a geospatial datastore and a timeseries datastore. It is a huge amount of data stored as a nested map. This is why Dataproc’s ability to provide a probabilistic approach to testing a deterministic system is really useful in a remediating / self-healing mobile network.
Apache Spark provides the parallel processing of the variant datastores as Resilient Distributed Datasets (RDDs). Modelling the baseline data for geospatial topology, coverage and time-based trials is not trivial. But the fundamental processing of huge datasets for improved RAN distribution is highly challenging but eventually highly beneficial.