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Results: 319
Number of items: 319
  • Mesarcik, M., Boonstra, A.-J., Meijer, C., Jansen, W., Ranguelova, E., & van Nieuwpoort, R. V. (2020). LOFAR dataset for deep learning assisted data Inspection for radio astronomy [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3702430
  • Ye, D., Zun, P., & Veen, L. (2021). ISR3D other inputs file for UQ tutorial paper [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4603912
  • Farshidi, S. (2022). An adaptable indexing pipeline for enriching meta information of datasets from heterogeneous repositories [Data set]. Mendeley Data. https://doi.org/10.17632/3yb7mhxtyf.1
  • Farshidi, S., & Zhao, Z. (2021, November 19). KMS-ENVRI [Data set]. Mendeley Data. https://doi.org/10.17632/n3khm4pnsd.1
  • Hsu, C., Martín-Pérez, J., Papagianni, C., & Grosso, P. (2024, September 25). Dataset for Elastic Resource Scaling [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13838059
  • Hsu, C., De Vleeschauwer, D., & Papagianni, C. (2024, September 25). Dataset for SLAs Decomposition [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13837936
  • Farshidi, S., & Steffens, L. (2024, June 26). Data-Driven Decision Model for Machine Learning Model Selection [Data set]. Mendeley Data. https://doi.org/10.17632/drh9669vc3.1
  • Chirkin, A. M., Belloum, A. S. Z., Kovalchuk, S. V., & Makkes, M. X. (2014). Execution time estimation for workflow scheduling. In Proceedings of WORKS 2014: the 9th Workshop on Workflows in Support of Large-Scale Science : November 16, 2014 : held in conjunction with SC14: The International Conference for High Performance Computing, Networking, Storage and Analysis : New Orleans, Louisiana, November 16-21, 2014 (pp. 1-10). IEEE Press. https://doi.org/10.1109/WORKS.2014.11
  • Wassing, T. J., De Vleeschauwer, D., & Papagianni, C. (2021). A Machine Learning Approach for Service Function Chain Embedding in Cloud Datacenter Networks. In 2021 IEEE 10th International Conference on Cloud Networking (CloudNet 2021): virtual conference, 8-10 November 2021 (pp. 26-32). IEEE. https://doi.org/10.1109/CloudNet53349.2021.9657124
  • Shi, Z., Zhou, H., Koulouzis, S., Rubia, C., & Zhao, Z. (2020). Co-located and Orchestrated Network Fabric (CONF): An Automated Cloud Virtual Infrastructure for Social Network Applications. In U. Schwardmann, C. Boehme, & D. B. Heras (Eds.), Euro-Par 2019: Parallel Processing Workshops: Euro-Par 2019 International Workshops, Göttingen, Germany, August 26–30, 2019 : revised selected papers (pp. 464-475). (Lecture Notes in Computer Science; Vol. 11997). Springer. https://doi.org/10.1007/978-3-030-48340-1_36
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