The topic of the presentation is “Intelligent Horizontal Scaling and Control-Aware Sizing in IIoT Edge Systems”
The growing adoption of distributed architectures in the Industrial Internet of Things (IIoT) has expanded the use of edge computing environments for running intelligent services and control systems. However, the variability of computational loads, resource constraints, and time requirements of these applications pose challenges for the dynamic management of infrastructure.
This talk will present the evolution of horizontal scaling strategies in Kubernetes environments, addressing intelligent mechanisms based on artificial intelligence techniques—including fuzzy logic, machine learning, and deep learning—to optimize resource allocation, predict computational workloads, and dynamically adapt to the demands of distributed systems. Next, we will discuss the specific challenges of running distributed control systems on edge infrastructures, highlighting the limitations of traditional scaling strategies based exclusively on CPU utilization. The presentation will delve into the concept of control-aware sizing, in which control quality (Quality of Control, QoC) metrics, temporal constraints, and computational costs are considered jointly to determine the appropriate number of shared controllers.
Experimental results will be presented, and prospects for the development of resource management mechanisms capable of integrating scalability, computational efficiency, and control requirements in IIoT systems will be discussed.
The speaker
The speaker, Marcelo Augusto Costa Fernandes, is an associate professor in the Department of Computer Engineering and Automation at the Federal University of Rio Grande do Norte (UFRN). He holds a Ph.D. in Electrical Engineering from the State University of Campinas (UNICAMP) and has served as a visiting researcher at King’s College London (United Kingdom), Harvard University (United States), and the University of Granada (Spain). He is currently the director of the Center for Artificial Intelligence and Data Science (nIACD) at the Digital Metropolis Institute (IMD/UFRN) and coordinator of the InovAI Lab. His research focuses on artificial intelligence, deep learning, digital signal processing, embedded systems, reconfigurable computing, and bioinformatics. He is an associate researcher at INCT ICoNIoT and participates in national and international projects focused on the development of intelligent systems and AI technologies.