Anomaly Detection for Mobile Network Management


MingXue Wang
Sidath Handurukande


With emerging Network Functions Virtualization (NFV) and Software Defined Networking (SDN) paradigms in Network Management (NM), new network devices and features can immediately become available. Available network resources and services can be altered and optimized in real time to gain the maximum benefit. However, this requires real time analytics information sent to SDN controllers rather than traditional manual offline or batch analytic outputs which are delivered on hourly or monthly basis in NM Systems. As a result, real time stream analytics is becoming a critical element for NM. In this paper, we describe a anomaly detection analytic engine for NM system development. We describe the design principles, innovative algorithm design, architecture and implementation of the engine in relation to streaming data and mobile NM. Finally, we present use cases and evaluation results.


How to Cite
MingXue Wang, & Sidath Handurukande. (2018). Anomaly Detection for Mobile Network Management. International Journal of Next-Generation Computing, 9(2), 80–98.


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