PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Ismaeel, S., Al-Khazraji, A., & Miri, A. (2019, April 15-17). An efficient workload clustering framework for large-scale data centers. [Paper presentation]. 8th International Conference on Modeling Simulation and Applied Optimization, Manama, Bahrain. https://doi.org/10.1109/ICMSAO.2019.8880305

• Ismaeel, S., & Miri, A. (2019, January 7-9). Real-time energy-conserving VM-provisioning framework for cloud-data centers. [Paper presentation]. IEEE 9th Annual Computing and Communication Workshop and Conference, Las Vegas, USA. https://doi.org/10.1109/CCWC.2019.8666614

• Jackson, K. R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., Wasserman, H. J., & Wright, N. J. (2010, November 30 – December 3). Performance analysis of high performance computing applications on the Amazon Web Services cloud. [Paper presentation]. IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, USA. https://doi.org/10.1109/CLOUDCOM.2010.69

• Mishra, A. K., Hellerstein, J. L., Cirne, W., & Das, C. R. (2010). Towards characterizing cloud backend workloads. ACM SIGMETRICS Performance Evaluation Review, 37(4), 34-41. https://doi.org/10.1145/1773394.1773400

• Moro, A., Mumolo, E., & Nolich, M. (2009, September 16-18). Ergodic continuous hidden markov models for workload characterization. [Paper presentation]. Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria. https://doi.org/10.1109/ISPA.2009.5297771

• Onan, A. (2019). Consensus Clustering-based undersampling approach to imbalanced learning. Scientific Programming, 2019, 1-14. https://doi.org/10.1155/2019/5901087

• Onan, A., & KorukoGlu, S. (2017). A feature selection model based on genetic rank aggregation for text sentiment classification. Journal of Information Science, 43(1), 25-38. https://doi.org/10.1177/0165551515613226

• Panneerselvam, J., Liu, L., Antonopoulos, N., & Bo, Y. (2014, December 8-11). Workload analysis for the scope of user demand prediction model evaluations in cloud environments. [Paper presentation]. IEEE/ACM 7th International Conference on Utility and Cloud Computing, London, United Kingdom. https://doi.org/10.1109/UCC.2014.144

• Patel, J., Jindal, V., Yen, I. L., Bastani, F., Xu, J., & Garraghan, P. (2015, March 25-27). Workload estimation for improving resource management decisions in the cloud. [Paper presentation]. IEEE 12th International Symposium on Autonomous Decentralized Systems, Taichung, Taiwan. https://doi.org/10.1109/ISADS.2015.17

• Rasheduzzaman, M., Islam, M. A., Islam, T., Hossain, T., & Rahman, R. M. (2014, February 21-22). Task shape classification and workload characterization of google cluster trace. [Paper presentation]. IEEE International Advance Computing Conference, Gurgaon, India. https://doi.org/10.1109/IADCC.2014.6779441

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• Shekhawat, V. S., Gautam, A., & Thakrar, A. (2018, December 1-2). Datacenter workload classification and characterization: An empirical approach. [Paper presentation]. IEEE 13th International Conference on Industrial and Information Systems, Rupnagar, India. https://doi.org/10.1109/ICIINFS.2018.8721402

• Shen, S., van Beek, V., & Iosup, A. (2015, May 4-7). Statistical characterization of business-critical workloads hosted in cloud datacenters. [Paper presentation]. IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, Shenzhen, China. https://doi.org/10.1109/CCGRID.2015.60

• Wang, K., Lin, M., Ciucu, F., Wierman, A., & Lin, C. (2015). Characterizing the impact of the workload on the value of dynamic resizing in data centers. Performance Evaluation, 85-86, 1-18. https://doi.org/10.1016/J.PEVA.2014.12.001

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• Zhang, Q., Hellerstein, J., & Boutaba, R. (2011) Characterizing task usage shapes in Google compute clusters. Google Research. https://research.google/pubs/pub37201/