Hybrid cloud infrastructure to handle large scale data for bangladesh people search (BDPS)

Author(s):  
Narzu Tarannum ◽  
Nova Ahmed
Author(s):  
Julia Velkova ◽  
Patrick Brodie

The past decade has seen the accelerated growth and expansion of large-scale data centre operations across the world to support emerging consumer and business data and computation needs. These buildings, as infrastructures responsive to changing global economic and technological terrain, are increasingly modular, and must be built out rapidly. However, these conditions also mean that their paths to obsolescence are shortened, their lifespans dependent on shifting corporate strategies and advances in consumer technology. This paper theorises and empirically explores material, infrastructural abandonment that emerges in this process of data centre construction across different geographical contexts. To do so, we analyse the socio-material construction of an international network of large-scale data centres by global telecom giant Ericsson, and the abrupt abandonment and suspension of one of its nodes in Vaudreuil, Québec in 2017 after only nine months of operation. Employing autoethnography, site visits, and qualitative interviews with data centre architects and staff in Sweden and Canada, we argue that the ruins of abandoned 'cloud' infrastructure represent the disjunction between the 'promise' of digital infrastructure for local communities and the market interests of digital companies. With its focus, the paper takes ruination and discard as perspectives through which to understand the complexity of emergent datafied futures and the socio-technical reshaping of internet infrastructures.


2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  

Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.


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