FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum

F. Filippini, R. Cavadini, D. Ardagna, R. Lancellotti, G. Russo Russo, V. Cardellini, F. Lo Presti

Proc. of 3rd International Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC 2023)

The widespread adoption of Artificial Intelligence applications to analyze data generated by Internet of Things sensors leads to the development of the edge computing paradigm. Deploying applications at the periphery of the network effectively addresses cost and latency concerns associated with cloud computing. However, it generates a highly distributed environment with heterogeneous devices, opening the challenges of how to select resources and place application com- ponents. Starting from a state-of-the-art design-time tool, we present in this paper a novel framework based on Reinforcement Learning, named FIGARO (reinForcement learnInG mAnagement acRoss the computing cOntinuum). It handles the runtime adaptation of a computing continuum environment, dealing with the variability of the incoming load and service times. To reduce the training time, we exploit the design-time knowledge, achieving a significant reduction in the violations of the response time constraint.