Analysis and intelligence capabilities play an important role to address 5G requirements. In combination with key-enabled technologies such as SDN, NFV, cloud computing, etc. All of these domains can take advantage of forecasting, pattern recognition, artificial intelligence and advanced intelligence concepts. 5G networks will be able to provide enhanced capacities related to the network management and the detection of possible harmful problems. For its part, the diagnosis of data information is required in order to know what the real cause of the event is. The intelligence is provided in two phases: (i) analysis stage and (ii) decision-making.
The SELFNET Analyzer framework is the first proposal that provides a generalized framework to deal with both traditional technologies and currently 5G key-enabled technologies. The proposed data specification to accommodate the onboarding of new use cases is simple and adjustable. The framework was developed to be able to operate indistinctly with very different data mining and machine learning paradigms. The use of any of them does not imply design changes, being simply an implementation problem. As a result, this proposal is easily adaptable to future projects.
The effectiveness of the Analyzer Module depends on the quality of their specification. The SELFNET Analyzer is not able to deal with complex stationary monitoring environments. There are a number of challenges that need to be addressed related to how the data received from underlying layers will be organized or how the analysis process will be performed. The investigation of methods to process and analyze the received information is also part of the ongoing work. This information will be loaded and converted into facts by the analyzer framework in order to provide the network state in real time.
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