Sustainable, QoS, and cost-aware placement of microservices on the continuum: a use case on the Internet of Medical Things

Domains such as healthcare, which are intensive in contrast to user-grade domains, are increasingly interested to develop Internet of Things (IoT) applications to automate their critical processes, leading to the Internet of Medical Things (IoMT). IoMT applications leverage the Computing Continuum infrastructure. However, for intensive IoT and IoMT applications to be feasible, their strict Quality of Service (QoS) requirements must be met, and the economic cost of their deployment must be low to ensure their business viability. Furthermore, sustainability and the reduction of the carbon footprint are currently a priority due to industrial and governmental initiatives and requirements, such as Green IoT or the Sustainable Development Goals. The efforts in sustainability are aimed not only at achieving energy-efficient applications, but carbon-aware ones, aligning carbon footprint reduction with high QoS and low economic cost. While all three objectives can be achieved by strategically placing the microservices of these IoT applications, navigating the trade-offs across the three is a complex issue, calling for automated solutions that provide IoT application developers with a manageable number of Pareto-optimal microservice placements. This work presents the Many-Objective Genetic Algorithm for Microservice Placement (MOGAMP), which leverages evolutionary computing to assist IoT application developers in navigating the QoS, cost, and sustainability trade-off in microservice placement. In an evaluation with an IoMT use case, MOGAMP is shown to be scalable, up to 459.82 times faster and with a memory footprint of up to 0.37% compared to alternatives, enabling IoT application developers to explore wide, yet manageable, Pareto fronts.