In the research project led by Professor Francisco Airton (UFPI), he and his students Iure Fé (PhD), José Miqueias (MSc) and Lucas Lopes (MSc) are developing analytical models based on Petri nets, which are used to mathematically represent any distributed system. They create a diagram describing the system and perform probabilistic calculations to obtain various metrics, using a specific tool for this purpose.
Airton believes that these models can also be interpreted as digital twins. Based on this, the group selects a specific information system — in the case of the PhD student, Kubernetes — and constructs Petri net models that represent the deployment of Kubernetes. After modelling this system, they integrate the model with software capable of running it and monitoring Kubernetes in real time.
The master’s students, meanwhile, use other monitored systems: one works with camera monitoring, and another with a drone simulator.
The team is preparing papers for this year’s SBRC, describing how the digital twin platform can outperform different types of autoscaling. In this case, the system tests different configurations and simulates ‘what-if’ scenarios, which allows the best option to be identified before applying it in the real environment.
In traditional client-server architecture, Kubernetes operates alongside servers and can connect to any client, including IoT devices. On these devices, a set of sensors generates data that varies depending on the context, such as the time of day or traffic flow. This variation in demand requires the Kubernetes deployment configuration to be adjusted dynamically. This is where K8s-DT comes into play, predicting the best new configuration to be implemented.