Telco Use Cases for Google Cloud Dataproc – managed Spark & Hadoop for Mobile Network Performance Data

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 RDDs

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.

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