Uma arquitetura de monitoramento de infraestruturas virtuais

• Ano de defesa: 2020
• Autor(a) principal: Cardoso, Lanay Marques
• Orientador(a): Salgueiro, Ricardo José Paiva de Britto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Programa de Pós-Graduação: Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Computação em nuvem; Monitoramento; REST
• Palavras-chave em Inglês: Application Programming Interface (API); Cloud computing; Monitoring
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://ri.ufs.br/jspui/handle/riufs/14549

In an increasingly connected world, cloud computing aggregates all kinds of information. With the growing number of online applications and the great prospect of increasing the services offered on the Internet, it is essential to continuously monitor the availability of services and applications, progressively susceptible to attacks cybernetics and other types of failure-causing events. Most monitoring software is proprietary, expensive and may not be scalable; in contrast, free software has a design that requires a longer learning time. In addition to the issues mentioned, there is a lack of functionality for monitoring APIs used by services and applications on the web, which are increasingly important with the consolidation of microservices architectures. As a proposal for a light and scalable monitoring model, this dissertation presents an adaptable architecture that can be applied to infrastructures in minutes, without deep interventions that affect the integrity of the original system. A plug-infoi module developed to monitor APIsREST. The monitoring data are shown on a graphic chart using the plug-in, identifying the availability of the applications. The data obtained from the status of these endpoints are stored in a real-time database. The monitoring architecture was implemented in a way that is adapted to the cloud environment, both public and private. The case studies carried out demonstrated the applicability of the solution, showing a general view of monitored services and allowing data analysis. The monitoring architecture has an adaptive distributed model, which is scalable and integrable with different tools, clouds and applications.