Today's Network Operation Centers (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network's health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today's networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors' technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this work, we investigate whether such decisions can be taken by Artificial Intelligence instead of collective human intelligence, specifically by Deep-Reinforcement Learning (DRL), which has been shown in computer games to outperform humans. We build an Action Recommendation Engine (ARE) based on RL, train it with expert rules or by letting it explore outcomes by itself, and show that it can learn new and more efficient strategies that outperform expert rules designed by humans by as much as 25%.

Made available in DSpace on 2016-04-04T18:31:22Z (GMT). No. of bitstreams: 1 Andrey Guedes Oliveira.pdf: 4354812 bytes, checksum: 17825c1f558c7ff18c82b21e9935d9fb (MD5) Previous issue date: 2008-02-14 / This work aims to describe the measurement the operational indicators in services attending and support problems realized by Network Operation Center, as well as, to establish attendance metrics directed towards contract maintenance, as well as, recommending the use of the simulations in the Operational Plans of the Service Information Technology Providers. The use of the Theory of Lines, eTOM (enhanced Telecom Operations Map) and ITIL (Information Technology Infrastructure Library) assisted in the analysis of the behavior of Network Operation Center. Had been used a real data from a Network Operation Center of the Datacenter, that it pertaining to a Services Telecommunications Provider. However, it is observed that the Theory of Lines and the simulations need a maximum value of attendance to be applied to the historical data of an operational team. The simulation by this attendance parameter allowed us to map the capacity of the Datacenter team with eight analysts, within acceptable values to fulfill Service Level Agreements up to 92% precision to support problems, 85% to analyze alarm tickets and 89% of utilization in a services attendance. The others simulations had projected the behavior of the team with two new scenes: ten and twelve analysts. The use of the computational simulations can be analyzed and be compared the simulated real data and with attendance projections, making possible an operational planning adjusted the new based business-oriented modalities in convergent networks. The computer simulations using maximum parameter, allowed a analyzing of services types behavior and establishing an operational planning. / Este trabalho tem por objetivo dimensionar recursos humanos nos Centros de Opera??es de Redes e estabelecer indicadores de atendimento voltados para a manuten??o dos contratos e a melhoria dos servi?os prestados, bem como utilizar simula??es computacionais no planejamento operacional para o Centro de Opera??es de Redes. As an?lises de atendimentos deste Centro de Opera??es foram realizadas por meio das melhores pr?ticas em gest?o de Tecnologia da Informa??o - ITIL (Information Technology Infrastructure Library), pelo mapa de processos de Telecomunica??es - eTOM (enhanced Telecom Operations Map) e pela Teoria das Filas. Para isto, foram utilizados dados hist?ricos e reais de um Centro de Opera??es de Rede de um Datacenter, pertencente a um Provedor de Servi?os de Tecnologia da Informa??o e Comunica??o. Foram analisados os dados de atendimentos via Teoria das Filas e comparado com os dados simulados. Notouse a necessidade de um valor m?ximo de atendimento para o dimensionamento da equipe, e esse par?metro serviu de base em novas simula??es que possibilitaram uma an?lise comparativa com proje??es de atendimento. A capacidade do grupo com os patamares aceit?veis, ao cumprimento dos Acordos de N?veis de Servi?os com oito analistas, ocorreu dentro de uma precis?o m?dia de 92% para solu??es a problemas, 85% para suporte a alarmes nos equipamentos e 89% de utiliza??o nas presta??es de servi?os. As demais simula??es projetaram o comportamento da equipe com dois novos cen?rios: dez e doze analistas. Os resultados propiciaram o estabelecimento de um planejamento operacional com os principais indicadores de atendimento e informa??es relevantes para a tomada de decis?es empresariais.