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Liu, H. (2024). Robust resource management for time-critical tasks in the cloud-edge continuum. [Thesis, fully internal, Universiteit van Amsterdam].
Cheng, L., He, H., Gu, Y., Liu, Q., Zhao, Z., & Fang, F. (2024). MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection. In 2024 IEEE 30th International Conference on Parallel and Distributed Systems: ICPADS 2024 : 10-14 October 2024, Belgrade, Serbia : proceedings (pp. 657-666). IEEE Computer Society. https://doi.org/10.1109/ICPADS63350.2024.00091
Spanjer, R. F., & Belloum, A. S. Z. (2024). Protocol-Level Kafka Controls in a Customizable Proxy. In EScience '24 proceedings: 2024 IEEE 20th International Conference on e-Science (e-Science) : September 16-20, 2024, Osaka, Japan (pp. 313-314). IEEE. https://doi.org/10.1109/e-Science62913.2024.10678687
Xin, R., Chen, P., Grosso, P., & Zhao, Z. (2024). A fine-grained robust performance diagnosis framework for run-time cloud applications. Future Generation Computer Systems, 155, 300-311. https://doi.org/10.1016/j.future.2024.02.014
Jiang, W., Luo, T., Liang, Z., Chen, K., He, J., Zhao, Z., Wen, J., Zhao, L., & Song, W. (2024). FBENet: Feature-Level Boosting Ensemble Network for Hashimoto’s Thyroiditis Ultrasound Image Classification. IEEE Journal of Biomedical and Health Informatics, 28(9), 5360–5369. https://doi.org/10.1109/jbhi.2024.3414389
van de Kamp, R., Bakker, K., & Zhao, Z. (2024). Paving the Path Towards Platform Engineering Using a Comprehensive Reference Model. In T. Prince Sales, S. de Kinderen, H. A. Proper, L. Pufahl, D. Karastoyanova, & M. van Sinderen (Eds.), Enterprise Design, Operations, and Computing. EDOC 2023 Workshops: IDAMS, iRESEARCH, MIDas4CS, SoEA4EE, EDOC Forum, Demonstrations Track and Doctoral Consortium, Groningen, The Netherlands, October 30–November 3, 2023 : revised selected papers (pp. 177–193). (Lecture Notes in Business Information Processing; Vol. 498). Springer. https://doi.org/10.1007/978-3-031-54712-6_11
Cheng, L., Chen, X., & Zhao, Z. (2024). Preface of special issue on Artificial Intelligence for time-critical computing systems. Future Generation Computer Systems, 159, 102-104. https://doi.org/10.1016/j.future.2024.05.011
Zhang, Z., Papagianni, C., Speelman, F., & Grosso, P. (2024). Toward Complete Quantum Network Stacks: A Survey. IEEE Network, 38(5), 153-159. https://doi.org/10.1109/MNET.2024.3402951
Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., & Zhao, Z. (2024). Autonomous selection of the fault classification models for diagnosing microservice applications. Future Generation Computer Systems, 153, 326-339. https://doi.org/10.1016/j.future.2023.12.005
Pongrácz, G., Mihály, A., Gódor, I., Laki, S., Nanos, A., & Papagianni, C. (2023). Towards extreme network KPIs with programmability in 6G. In MobiHoc '23: Proceedings of the 2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing : October 23-26, 2023, Washington, DC, USA (pp. 340-345). Association for Computing Machinery. https://doi.org/10.1145/3565287.3617610

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