Browsing by Author "Nguyen, T. H."
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- The I4U mega fusion and collaboration for NIST speaker recognition evaluation 2016
A4 Artikkeli konferenssijulkaisussa(2017) Lee, K. A.; Hautamäki, V.; Kinnunen, T.; Larcher, A.; Zhang, C.; Nautsch, A.; Stafylakis, T.; Rouvier, M.; Rao, W.; Alegre, F.; Ma, J.; Mak, M. W.; Sarkar, A. K.; Delgado, H.; Saeidi, R.; Aronowitz, H.; Sizov, A.; Sun, H.; Nguyen, T. H.; Wang, G.; Ma, B.; Vestman, V.; Sahidullah, M.; Halonen, M.; Kanervisto, A.; Le Lan, G.; Bahmaninezhad, F.; Isadskiy, S.; Rathgeb, C.; Busch, C.; Tzimiropoulos, G.; Qian, Q.; Wang, Z.; Zhao, Q.; Wang, Tianzhou; Li, H.; Xue, J.; Zhu, S.; Jin, R.; Zhao, T.; Bousquet, P. M.; Ajili, M.; Kheder, W. B.; Matrouf, D.; Lim, Z. H.; Xu, C.; Xu, H.; Xiao, X.; Chng, E. S.; Fauve, B.; Sriskandaraja, K.; Sethu, V.; Thomsen, D. A.L.; Tan, Z. H.; Todisco, M.; Evans, N.; Li, Haizhou; Hansen, J. H.L.; Bonastre, J. F.; Ambikairajah, E.; Liu, Gang; Lin, WeiweiThe 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration. - Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centers
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020-01) Nguyen, T. H.; Francesco, M. Di; Yla-Jaaski, A.Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.