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Results: 229
Number of items: 229
  • Elzinga, O., Koulouzis, S., Taal, A., Wang, J., Hu, Y., Zhou, H., Martin, P., de Laat, C., & Zhao, Z. (2017). Automatic collector for dynamic cloud performance Information. In 2017 IEEE International Conference on Networking, Architecture, and Storage (NAS): proceedings : 7-9 August 2017, Shenzhen, China (pp. 226-231). IEEE. https://doi.org/10.1109/NAS.2017.8026845
  • Koulouzis, S., Belloum, A. S. Z., Bubak, M. T., Zhao, Z., Živković, M., & de Laat, C. T. A. M. (2016). SDN-aware federation of distributed data. Future Generation Computer Systems, 56, 64-76. https://doi.org/10.1016/j.future.2015.09.032
  • Zhou, H., Hu, Y., Wang, J., Martin, P., de Laat, C., & Zhao, Z. (2016). Fast and Dynamic Resource Provisioning for Quality Critical Cloud Applications. In 2016 IEEE 19th International Symposium on Real-Time Distributed Computing : proceedings: ISORC 2016 : 17-20 May 2016, York, United Kingdom (pp. 92-99). IEEE Computer Society. https://doi.org/10.1109/ISORC.2016.22
  • Martin, P., Taal, A., Quevedo, F., Rogers, D., Evans, K., Jones, A., Stankovski, V., Taherizadeh, S., Trnkoczy, J., Suciu, G., & Zhao, Z. (2016). Information modelling and semantic linking for a software workbench for interactive, time critical and self-adaptive cloud applications. In L. Barolli, M. Takizawa, T. Enokido, A. J. Jara, & Y. Bocchi (Eds.), Proceedings IEEE 30th International Conference on Advanced Information Networking and Applications Workshops: WAINA 2016 : 23-25 March 2016, Crans-Montana, Switzerland (pp. 127-132). IEEE Computer Society. https://doi.org/10.1109/WAINA.2016.38
  • Zhou, H., Wang, J., Hu, Y., Su, J., Martin, P., de Laat, C., & Zhao, Z. (2016). Fast Resource Co-provisioning for Time Critical Application Based on Networked Infrastructures. In I. Foster, & N. Radia (Eds.), Proceedings, 2016 IEEE 9th International Conference on Cloud Computing: CLOUD 2016 : 27 June-2 July 2016, San Francisco, California, USA (pp. 802-805). IEEE Computer Society, Conference Publishing Services. https://doi.org/10.1109/CLOUD.2016.0111
  • Open Access
    Petcu, D., Fazio, M., Prodan, R., Zhao, Z., & Rak, M. (2016). On the Next Generations of Infrastructure-as-a-Services. In J. Cardoso, D. Ferguson, V. Méndez Muñoz, & M. Helfert (Eds.), CLOSER 2016: proceedings of the 6th International Conference on Cloud Computing and Services Science: April 23-25, 2016, Rome, Italy (Vol. 1, pp. 320-326). SciTePress Science and Technology Publications. https://doi.org/10.5220/0005912503200326
  • Open Access
    Taherizadeh, S., Jones, A., Taylor, I., Zhao, Z., Martin, P., & Stankovski, V. (2016). Runtime Network-level Monitoring Framework in the Adaptation of Distributed Time-critical Cloud Applications. In H. Arabnia, H. Ishii, K. Joe, & H. Nishikawa (Eds.), PDPTA 2016: proceedings of the 2016 International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, Nevada, USA, July 25-28, 2016 (pp. 78-83). CSREA Press. https://doi.org/10.5281/zenodo.53869
  • Open Access
    Casale, G., Chesta, C., Deussen, P., Di Nitto, E., Gouvas, P., Koussouris, S., Stankovski, V., Symeonidis, A., Vlassiou, V., Zafeiropoulos, A., & Zhao, Z. (2016). Current and Future Challenges of Software Engineering for Services and Applications. Procedia Computer Science, 97, 34–42. https://doi.org/10.1016/j.procs.2016.08.278
  • Open Access
    Zhao, Z., Martin, P., de Laat, C., Jeffery, K., Jones, A., Taylor, I., Hardisty, A., Atkinson, M., Zuiderwijk-van Eijk, A., Yin, Y., & Chen, Y. (2016). Time critical requirements and technical considerations for advanced support environments for data-intensive research. Paper presented at International workshop on Interoperable infrastructures for interdisciplinary big data sciences, Porto, Portugal. https://doi.org/10.5281/zenodo.204756
  • Open Access
    Zhou, H., Martin, P., Su, J., de Laat, C., & Zhao, Z. (2016). A Flexible Inter-locale Virtual Cloud For Nearly Real-time Big Data Applications. Paper presented at International workshop on Interoperable infrastructures for interdisciplinary big data sciences, Porto, Portugal. https://doi.org/10.5281/zenodo.204774
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