Ilager, S., Balouek, D., Kaddour, S. M., & Brandic, I. (2024). Proteus: Towards Intent-driven Automated Resource Management Framework for Edge Sensor Nodes. In FlexScience’24 : Proceedings of the 14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3659995.3660037 ( reposiTUm)

Catalfamo, A., Aral, A., Brandic, I., Deelman, E., & Villari, M. (2024). Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. In Computational Science – ICCS 2024 : 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V (pp. 368–382). Springer. https://doi.org/10.1007/978-3-031-63775-9_27 ( reposiTUm)

Tundo, A., Mobilio, M., Ilager, S. S., Brandic, I., Bartocci, E., & Mariani, L. (2023). An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 281–293). IEEE. https://doi.org/10.1109/ASE56229.2023.00046 ( reposiTUm)

De Maio, V., Aral, A., & Brandic, I. (2022). A Roadmap To Post-Moore Era for Distributed Systems. In ApPLIED ’22: Proceedings of the 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems (pp. 30–34). Association for Computing Machinery (ACM). https://doi.org/10.1145/3524053.3542747 ( reposiTUm)

Tocze, K., Schmitt, N., Kargén, U., Aral, A., & Brandic, I. (2022). Edge Workload Trace Gathering and Analysis for Benchmarking. In 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC) (pp. 34–41). IEEE. https://doi.org/10.1109/ICFEC54809.2022.00012 ( reposiTUm)

Zhou, H., Aral, A., Brandic, I., & Erol-Kantarci, M. (2021). Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures. arXiv. https://doi.org/10.48550/arXiv.2111.11868 ( reposiTUm)