Venâncio Neto, Augusto JoséSantos, Charles Hallan Fernandes dos2024-09-052024-09-052024-04-30SANTOS, Charles Hallan Fernandes dos. Recuperação inteligente de desastres em sistemas de operação, gerenciamento e controle de infraestruturas 5G. Orientador: Prof. Dr. Augusto José Venâncio Neto. 2024. 84f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2024.https://repositorio.ufrn.br/handle/123456789/60044Management solutions for telecommunications ecosystems based on fifth-generation (5G) networks must be able to control infrastructure resources in a granular and automated manner. When adopting a management model, service providers must aim to meet the quality requirements of their customers. With this in mind, operators employ Operation, Management and Control (OMC) centers, an ecosystem that involves different technologies and tools that interoperate to provide operational functions designed to guarantee service level agreements (SLA) on an ongoing basis. In this sense, the unavailability of an OMC can represent a disaster for the service provider and its users, given the dependence of the devices on the telecommunications infrastructure. To ensure fault tolerance, disaster recovery systems (DRS) must operate on redundant instances of the OMC, where the backup unit must take over when its primary instance fails. A DRS can adopt proactive features that detect the disaster before it occurs, maximizing the availability of the OMC. In addition, the adoption of multiple backup instances has the potential to increase the recovery possibilities of a failed OMC. In this sense, a DRS must incorporate selection mechanisms to determine the best backup to take over operations. Considering a DRS with proactive failure detection, the selection algorithm must choose the backup OMC that performs the transition of its execution and its data before the disaster occurs, in order to increase its availability. In order to select the most appropriate backup OMC (i.e. candidate), it is necessary to carry out a survey of performance indices that determine whether a candidate is capable of transferring the main OMC within an estimated time for the failure to occur. Given the absence of similar mechanisms in the literature used in the context of disaster detection and recovery, this master’s research is dedicated to exploring Machine Learning techniques to develop a mechanism for selecting backup OMCs. In this context, ML is used to estimate the period required to migrate an OMC for each candidate, in the hypothesis of reducing or eliminating data losses by choosing the best backup OMC. To this end, iDRS (intelligent DRS) is introduced, which is based on intelligent mechanisms to act in the assignment of control of OMCs, in the hypothesis of provisioning a system available to maintain 5G networks throughout their useful life. iDRS collects multiple pieces of information that influence service migration to candidate locations, such as network performance metrics and virtualized infrastructure computing resources. From this, an ML algorithm categorizes the candidates in terms of migration time estimates. A case study based on an emulated testbed attests to the effectiveness of iDRS in terms of OMC data integrity compared to state-of-the-art algorithms.Acesso AbertoComputação5GRecuperação de desastresSoluções de gerenciamentoMachine learningMigração de funçõesmasterThesisCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO