Active Inference and Digital Twins for dynamic reconfiguration of software systems

Abstract

In the following, the artefacts used during the experiments described in the article titled "Active Inference and Digital Twins for dynamic reconfiguration of software systems" are provided. The experiments were designed to evaluate and compare a Digital Twin enhanced with Active Inference (AIF-enhanced DT) proposal with another resource manager, called Proactive Latency-aware Adaptation (PLA). The article focuses on the development of the DT and the comparison between both reconfiguration managers with the aim of determining which of the two provides a better parameter configuration for the system in which they have been tested. The artifacts included are the followings: tuning_hyperparameters_runs.xlsx: This file contains the results of the 30 runs performed for each combination of Active Inference hyperparameters. Each sheet represents a different configuration. tuning_means_runs.xlsx: This file contains the average of the 30 runs performed for each combination of Active Inference hyperparameters. final_results_trace_0.xlsx: This file contains the average results of the 30 runs performed for each reconfiguration manager evaluated in the times series evaluation section of the paper. The Adapt variable distinguishes between them, with a value of 0 for the AIF-enhanced DT and 1 for PLA. Traces_1_2_3_results.xlsx: This file contains the average results of the 30 runs performed for each reconfiguration manager evaluated in the statistical comparison section of the paper. The Adapt variable distinguishes between them, with a value of 0 for the AIF-enhanced DT and 1 for PLA. B_matrix_complete: This file includes the complete version of the B matrix for the Active Inference Algorithm. This paper is part of the Grant PID2022-140907OB-I00 funded by MICIU/AEI/10.13039/501100011033 and ERDF, EU. It has also been partially supported by the Junta de Comunidades de Castilla-La Mancha/ERDF (SBPLY/24/180225/000020), by the University of Castilla-La Mancha (2025-GRIN-38441) and by the Cátedra Ciudad de Albacete (13585/2025). Elena Pretel holds a FPU21/02679 Scholarship from Spanish \textit{Ministerio de Educación y Formación Profesional} and holds a EST25/00141 scholarship from Spanish Ministerio de Ciencia, Innovación y Universidades. Dustdar‘s work and equipment has been supported by CNS2023-144359 financed by MICIU/AEI/10.13039/501100011033 and the European Union NextGeneration EU/PRTR. Many thanks to Gabriel A. Moreno for his help in developing the project.