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Marcelino, C., Guelmino, L., Pusztai, T., & Nastic, S. (2025). Databelt: A continuous data path for serverless workflows in the 3D compute continuum. Journal of Systems Architecture, 168, Article 103577. https://doi.org/10.1016/j.sysarc.2025.103577 ( reposiTUm)

Stanisic, A., Gravara, M., Herrera Gonzalez, J. L., & Nastic, S. (2027). Constella: A Novel Framework for Cost-Efficient Distributed AI Inference in LEO Space Data Centers. In M. Torquati, G. Mencagli, V. Cardellini, A. Antelmi, & D. Medić (Eds.), Euro-Par 2026: Parallel Processing : 32nd European Conference on Parallel and Distributed Processing : Proceedings, Part II (pp. 347–362). Springer Cham. https://doi.org/10.1007/978-3-032-35251-4_24 ( reposiTUm)

Gravara, M., Herrera Gonzalez, J. L., & Nastic, S. (2026). Compass: Optimizing Compound AI Workflows for Dynamic Adaptation. In 2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 84–93). IEEE. https://doi.org/10.1109/CCGrid68966.2026.00018 ( reposiTUm)

Gravara, M., Marcelino, C., Stanisic, A., & Nastic, S. (2026). PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum. In 2026 IEEE International Conference on Smart Computing Workshops and Other Affiliated events (SmartComp Companion) (pp. 261–266). IEEE. https://doi.org/10.1109/SmartComp-Companion70724.2026.00061 ( reposiTUm)

Arcanjo Marcelino, C. K., Gollhofer-Berger, S., Pusztai, T. W., & Nastic, S. (2025). Cosmos: A Cost Model for Serverless Workflows in the 3D Compute Continuum. In 2025 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 106–113). IEEE. https://doi.org/10.1109/SMARTCOMP65954.2025.00072 ( reposiTUm)

Pusztai, T. W., Hisberger, J., Arcanjo Marcelino, C. K., & Nastic, S. (2025). Stardust: A Scalable and Extensible Simulator for the 3D Continuum. In Proceedings of the IEEE International Conference on Edge Computing and Communications (IEEE EDGE 2025) (pp. 44–53). IEEE. https://doi.org/10.1109/EDGE67623.2025.00014 ( reposiTUm)

Kaya, M. C., Pusztai, T. W., Stanisic, A., & Nastic, S. (2025). Currus - A Compound AI Approach to Distributed Vehicle Trajectory Reconstruction in the Edge-Cloud. In S. Nastic, F. Michahelles, S. Ristov, P. Dazzi, & F. Wolling (Eds.), Proceedings of the 15th International Conference on the Internet of Things 2025 (pp. 254–262). Association for Computing Machinery. https://doi.org/10.1145/3770501.3770531 ( reposiTUm)

Marcelino, C., Krennmair, N., Pusztai, T. W., & Nastic, S. (2025). Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum. In S. Nastic, F. Michahelles, S. Ristov, P. Dazzi, & F. Wolling (Eds.), Proceedings of the 15th International Conference on the Internet of Things 2025 (pp. 113–121). Association for Computing Machinery. https://doi.org/10.1145/3770501.3770515 ( reposiTUm)

Reisecker, M., Marcelino, C., Pusztai, T., & Nastic, S. (2025). Gaia: Hybrid Hardware Acceleration for Serverless AI in the 3D Compute Continuum. In BDCAT ’25 : Proceedings of the IEEE/ACM 12th International Conference on Big Data Computing, Applications and Technologies. IEEE/ACM 12th International Conference on Big Data Computing, Applications and Technologies (BDCAT 2025), Nantes, France. ACM. https://doi.org/10.1145/3773276.3774299 ( reposiTUm)

Marcelino, C., Pusztai, T., & Nastic, S. (2025). Roadrunner: Accelerating Data Delivery to WebAssembly-Based Serverless Functions. In M. Sadoghi & L. Chen (Eds.), Middleware ’25: Proceedings of the 26th International Middleware Conference (pp. 354–368). Association for Computing Machinery. https://doi.org/10.1145/3721462.3770777 ( reposiTUm)

Helcig, M., & Nastic, S. (2025). FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting. In 2025 IEEE 9th International Conference on Fog and Edge Computing (ICFEC) (pp. 50–57). IEEE. https://doi.org/10.1109/ICFEC65699.2025.00012 ( reposiTUm)

Raith, P. A., Furutanpey, A., Lukić, N., Thurimella, V., & Nastic, S. (2025). EdgeCloudForge: Simulation-Driven Synthetic Dataset Generation For Proactive Serverless Edge Function Autoscaling. In 2025 IEEE International Conference on Cloud Engineering (IC2E) (pp. 126–135). IEEE. https://doi.org/10.1109/IC2E65552.2025.00028 ( reposiTUm)

Marcelino, C., Gollhofer-Berger, S., Pusztai, T., & Nastic, S. (2025). Cosmos: A Cost Model for Serverless Workflows in the 3D Compute Continuum. arXiv. https://doi.org/10.34726/10220 ( reposiTUm)

Pusztai, T., Hisberger, J., Marcelino, C., & Nastic, S. (2025). Stardust: A Scalable and Extensible Simulator for the 3D Continuum. arXiv. https://doi.org/10.34726/10241 ( reposiTUm)

Gravara, M., Stanisic, A., & Nastic, S. (2025). A Novel Compound AI Model for 6G Networks in 3D Continuum. arXiv. https://doi.org/10.34726/10420 ( reposiTUm)

Helcig, M. A., & Nastic, S. (2025). FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting. arXiv. https://doi.org/10.34726/10421 ( reposiTUm)