Real-Time Demonstration of Homodyne Coherent Bidirectional Transmission for Next-Generation Data Center Interconnects

2021 ◽  
Vol 39 (4) ◽  
pp. 1231-1238
Author(s):  
Tao Gui ◽  
Xuefeng Wang ◽  
Ming Tang ◽  
Yi Yu ◽  
Yanzhao Lu ◽  
...  
Author(s):  
Mitsutaka Kimura ◽  
Mitsuhiro Imaizumi ◽  
Toshio Nakagawa

Recently, cloud computing has been widely used for the purpose of protecting client data on the Internet [A. Weiss, Computing in the clouds, netWorker11 (2007) 16–25; M. Armbrust et al., Above the clouds: A Berkeley view of cloud computing, Technical Report UCV/EECS-2009-28, University of California at Berkeley (2009)]. But when a client receives network service, response time may be slow because the data center is located in a remote place. In order to solve the problem, real-time distributed systems for cloud computing has been proposed [M. Okuno, D. Ito, H. Miyamoto, H. Aoki, Y. Tsushima and T. Yazaki, A study on distributed information and communication processing architecture for next generation cloud system, IEICE Tech. Report109(A48) (2010) 241–246; M. Okuno, S. Tsutsumi and T. Yazaki, A study of high available distributed network processing technique for next generation cloud system, IEICE Tech. Report111(8) (2011) 25–30; S. Yamada, J. Marukawa, D. Ishii, S. Okamoto and N. Yamanaka, A study of parallel transmission technique with GMPLS in intelligent cloud network, IEICE Tech. Report109(455) (2010) 51–56]. The cloud computing system consists of some intelligent nodes as well as a data center. The data center manages all client data. The intelligent node provides client service near clients. It enables to provide client service at short response time [M. Okuno, D. Ito, H. Miyamoto, H. Aoki, Y. Tsushima and T. Yazaki, A study on distributed information and communication processing architecture for next generation cloud system, IEICE Tech. Report109(448) (2010) 241–246]. We considered the reliability model of distributed information processing for cloud computing, derived cost effectiveness and discussed the optimal replication interval to minimize it [M. Kimura, M. Imaizumi and T. Nakagawa, Reliability modeling of distributed information processing for cloud computing, in Proc. 20th ISSAT Int. Conf. Reliability and Quality in Design (2014), pp. 183–187]. Authors had dealt with the server system with one failure mode. In this paper, we consider the reliability model of a real-time distributed system with n intelligent nodes and formulate a stochastic model of the server system with n intelligent nodes for changing the other normal intelligent node at failure. We derive the expected numbers of the replication and of updating the client data. Further, we derive the expected cost and discuss an optimal replication interval to minimize it. Next, we derive the cost effectiveness and discuss an optimal number of intelligent nodes to minimize it.


Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 461
Author(s):  
Madjid Morsli ◽  
Quentin Kerharo ◽  
Jeremy Delerce ◽  
Pierre-Hugues Roche ◽  
Lucas Troude ◽  
...  

Current routine real-time PCR methods used for the point-of-care diagnosis of infectious meningitis do not allow for one-shot genotyping of the pathogen, as in the case of deadly Haemophilus influenzae meningitis. Real-time PCR diagnosed H. influenzae meningitis in a 22-year-old male patient, during his hospitalisation following a more than six-metre fall. Using an Oxford Nanopore Technologies real-time sequencing run in parallel to real-time PCR, we detected the H. influenzae genome directly from the cerebrospinal fluid sample in six hours. Furthermore, BLAST analysis of the sequence encoding for a partial DUF417 domain-containing protein diagnosed a non-b serotype, non-typeable H.influenzae belonging to lineage H. influenzae 22.1-21. The Oxford Nanopore metagenomic next-generation sequencing approach could be considered for the point-of-care diagnosis of infectious meningitis, by direct identification of pathogenic genomes and their genotypes/serotypes.


2008 ◽  
Author(s):  
Xiaoyun Zhu ◽  
Don Young ◽  
Brian J. Watson ◽  
Zhikui Wang ◽  
Jerry Rolia ◽  
...  

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