Sharing the application at the edge can cause latency issues. However, research by Professor Marcelo Fernandes of INCT ICoNIoT has shown that, depending on the control system, sharing is feasible
Historically, control systems in industrial plants (such as tanks in the chemical processing industry, industrial motors, production lines, and others) involved fixed, dedicated control equipment located near the plant. However, the vast majority of this equipment is undergoing a process of virtualization, becoming software. In modern edge computing architecture, this control equipment can be transformed into microservices in the form of applications running on edge servers. This transition offers a crucial advantage: the ability to share resources. Whereas physical equipment was previously required for each element of the plant (for example, one piece of equipment for each tank), the edge application can now be shared across multiple tanks. This leads to significant cost and energy savings.
However, the shift to shared microservices presents a new challenge: latency. Previously, the proximity of control equipment and industrial IoT devices ensured low latency. Sharing the application at the edge can introduce latency issues. However, research by Professor Marcelo Fernandes of INCT ICoNIoT has shown that, depending on the control system, sharing is feasible. For example, inherently slow systems, such as temperature or level control, tolerate latency and can be shared without issues. Marcelo Fernandes’s line of research, focused on edge computing and industrial IoT, aims precisely to use AI techniques for horizontal scaling. Horizontal scaling refers to the use of AI to automatically increase or decrease the number of these control applications, ensuring that the system continues to function properly.
Hypothesis Confirmed and Next Steps
Articles based on Dr. Fernandes’s research confirm the hypothesis that there are situations in which it is possible to share these control systems (emulating industrial plants) while ensuring they continue to function properly. The next step is crucial: using machine learning to perform horizontal scaling specifically in the context of industrial IoT plants. This is necessary because the parameters analyzed for horizontal scaling differ in each specific situation.
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Professor Marcelo Fernandes’ work also includes collaboration with Professor Debora Saade (UFF) in the field of Health, demonstrating the potential for exchange and interdisciplinary collaboration fostered by INCT ICoNIoT.