Abstract
Limitations in the software architecture of current network management tools such as lack of support for combined batch and real time data processing, adaptive machine learning, support for heterogeneous data sources and the fragmentation of emerging solutions needs to be addressed in order to create a solid and forward leaning foundation for implementing 5G solutions. To address these limitations, this paper introduces the extended lambda architecture (ELA). It focuses on bringing agility and continuous learning based decision making support into the design of a unified architectural framework for new network management tools by combining batch and real time data processing with adaptive machine learning in a simple Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) loop. The benefits of using this architecture are evaluated using a proof of concept (PoC) implementation of a reliable and proactive tool for detection and compensation of cell outages in a simulated 5G network.
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