RIBEIRO, V. T. R.; http://lattes.cnpq.br/2158210262282087; RIBEIRO, Vitor Trindade Rocha.
Abstract:
Telemedicine has emerged as an essential alternative for delivering healthcare services remotely, particularly during health crises such as the COVID-19 pandemic. However, traditional infrastructure faces challenges in managing the increasing demand, requiring solutions that provide low latency and high availability. Edge computing offers a promising approach by bringing processing closer to end users, enhancing efficiency and reducing latency. This work presents the evaluation and implementation of a distributed architecture based on edge computing to enable the dynamic provisioning of telemedicine services, focusing on virtual consultations and AI-assisted diagnostics. The proposed architecture integrates virtualized service orchestration technologies, facilitating the management of microservices and the dyna-mic allocation of resources. A proof-of-concept implementation and evaluation are carried out using Kubernetes to manage the services, ensuring a scalable and resilient system. The results demonstrate that edge computing can optimize telemedicine services, delivering higher-quality care.
