2024W

• Advanced Internet Computing / 184.269 / VU
• Distributed Systems / 194.024 / VU
• Project in Computer Science 1 / 194.145 / PR
• Project in Computer Science 2 / 194.146 / PR
• Seminar in Distributed Systems / 184.194 / SE

2025S

• Project in Computer Science 1 / 194.145 / PR
• Project in Computer Science 2 / 194.146 / PR

• faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms / Raith, P. A., Rausch, T., Furutanpey, A., & Dustdar, S. (2023). faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms. Software: Practice and Experience, 53(12), 2327–2361. https://doi.org/10.1002/spe.3277
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• Architectural Vision for Quantum Computing in the Edge-Cloud Continuum / Furutanpey, A., Barzen, J., Bechtold, M., Dustdar, S., Leymann, F., Raith, P., & Truger, F. (2023). Architectural Vision for Quantum Computing in the Edge-Cloud Continuum. In S. Ali, C. Ardagna, N. Atukorala, J. Barzen, C. K. Chang, Chang Rong N., J. Fan, I. Faro, S. Feld, G. Fox, Z. Jin, F. Leymann, F. Neukart, S. de la Puente, & M. Wimmer (Eds.), Proceedings of the IEEE International Conference on Quantum Software (IEEE QSW 2023) (pp. 88–103). IEEE. https://doi.org/10.1109/QSW59989.2023.00021
• Architectural Vision for Quantum Computing in the Edge-Cloud Continuum / Furutanpey, A., Barzen, J., Bechtold, M., Dustdar, S., Leymann, F., Raith, P. A., & Truger, F. (2023). Architectural Vision for Quantum Computing in the Edge-Cloud Continuum. arXiv. https://doi.org/10.34726/5940
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• FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing / Furutanpey, A., Raith, P. A., & Dustdar, S. (2023). FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing. arXiv. https://doi.org/10.48550/arXiv.2302.10681
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• Adaptive and Collaborative Inference: Towards a No-compromise Framework for Distributed Intelligent Systems / Furutanpey, A., & Dustdar, S. (2022). Adaptive and Collaborative Inference: Towards a No-compromise Framework for Distributed Intelligent Systems. In S. Decker, F. J. Domínguez Mayo, M. Marchiori, & J. Filipe (Eds.), Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022) (pp. 144–151). SciTePress. https://doi.org/10.5220/0011547800003318
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• An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques / Raith, P., Rausch, T., Prüller, P., Furutanpey, A., & Dustdar, S. (2022). An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques. In 2022 IEEE International Conference on Cloud Engineering (IC2E) (pp. 22–28). IEEE. https://doi.org/10.1109/IC2E55432.2022.00010
• Persistable, distributable and versionable application state for cloud service emulators / Furutanpey, A. (2022). Persistable, distributable and versionable application state for cloud service emulators [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.96467
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• A Serverless Computing Fabric for Edge & Cloud / Nastic, S., Raith, P., Furutanpey, A., Pusztai, T., & Dustdar, S. (2022). A Serverless Computing Fabric for Edge & Cloud. In Proceedings 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) (pp. 1–12). IEEE. https://doi.org/10.1109/CogMI56440.2022.00011

• Lossless neural compression for resource constrained environments: Using deep latent probabilistic models with bits-back asymmetric numerical systems / Bauernfeind, F. (2023). Lossless neural compression for resource constrained environments: Using deep latent probabilistic models with bits-back asymmetric numerical systems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.107248
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