Self-Adaptive Intelligent Deployment of Message Brokers: An Empirical Study on IoT Performance

Abstract

The Internet of Things (IoT) encompasses diverse characteristics, such as varying load frequencies and performance requirements. Designing static IoT systems to cover such varying loads is challenging. Self-adaptive systems, enhanced by artificial intelligence, can offer better performance by responding dynamically to changing conditions. Empirical research is essential to validate such systems. In this paper, we contribute by designing experiments to assess the performance of multiple IoT architectures under various load frequencies. Using the empirical data collected, we train an artificial neural network to predict response times for untested frequencies, identifying optimal scenarios for self-adaptive architecture transitions. We present a dataset of 2,641,008 points regarding the response times of requests for several industrial IoT deployment architectures. We perform an extensive systematic evaluation of 4,374 cases indicating 29.6% improvements in terms of reducing mean response times. Additionally, we provide prototypical tool support for practical implementations and to make our approach easy-to-use.