Experimentation platform and algorithm for network and application diagnostics in 5G

Abstract

5G networks will support demanding services such as enhanced Mobile Broadband, Ultra-Reliable and Low Latency Communications and massive Machine-Type Communications, which will require data rates of tens of Gbps, latencies of few milliseconds and connection densities of millions of devices per square kilometer. In order all these above services to be reliable in 5G networks there is an increase in the interest in software solutions that will help to provide this reliability. Therefore, root cause analysis and performance diagnosis has been gaining popularity in order to find effective methods to provide reliability to the 5G services. These methods consist of two major aspects, prediction and localize faults and service degradations that will help network engineers to make fact-based decisions on how to improve the system or mitigate the possible faults. In this master thesis we implement a performance diagnostics platform which implements an algorithm based on adjacency lists to perform Root Cause Analysis (RCA).