SINET5: A low-latency and high-bandwidth backbone network for SDN/NFV Era

Recent trends in MPLS networks: technologies, applications and challenges

“…SDN is also [21] 2018 further improved the load balancing and resource utilisation of the network with SDN Huang et al [23] 2018 overcame the label space reduction problem in MPLS network Guo et al [20] 2017 improved measurement error issues when using the single traffic matrix Mazhin et al [24] 2017 improved network flexibility MPLS-TE Premkumar and Saminadan. [2] 2017 enhanced network resiliency for sensitive protection data MPLS DiffServ Eugen [5] 2017 presented simulation results that proved the benefits of DiffServ in terms of delay integration of MPLS-TP Kurimoto et al [6] 2017 reconfigurable optical adddrop multiplexers to create nationwide burden with SDN Kurimoto et al [37] 2016 intra-and inter-domain network-enabled generic model transport for SDN and MPLS-TP network Lim et al [36] 2016 automatic topology discovery in the MPLS-TP network MPLS-TP protection Kim et al [44] 2018 achieved fast traffic recovery and relieved the communication burden Kolhar et al [40] 2016 reduced fault recovery time by 27 ms and decreased traffic load Blair et al [42] 2016 achieved 50 ms protection switching time Bächli et al [43] 2016 achieved guaranteed performance for critical application even under extreme conditions Choi [48] 2016 significant growth of data buffer hit ratio Kim et al [55] 2016 network availability subject to the maximised subprotection switching time of 50 ms Pijanka and Rózański. [34] 2016 proposed mobile MPLS-TP concept reduced the handoff speed compared with classic mobile MPLS MPLS-TP QoS Haddaji et al [3] 2018 laid out the challenges faced by service providers to migrate from legacy SDH to MPLS-TP Sakamoto et al [56] 2016 proved that the dynamic policing control will efficiently share the bandwidth for different services IET Commun., 2020, Vol.…”

“…Similar to the MPLS network, MPLS-TP is integrated with SDN to improve network flexibility and integrity by offering programmability, which is provided by SDN. Related works have proposed a resource pooling mechanism to improve network agility [35], implemented a virtual router based on SDN transport with automatic topology discovery function with MPLS-TP network [36], developed a generic model transport for SDN and MPLS-TP that enables easy operation of intra-and inter-domain network service [6], and reviewed a backbone network that uses MPLS-TP and reconfigurable optical add-drop multiplexers to create a nationwide network with new SDN-oriented on-demand VPN functions [37,38].…”

“…Given the considerable benefits of MPLS networks, research is being conducted to ensure that high-bandwidth demand can be addressed. Several related reviews on MPLS networks [3][4][5][6][7], with their advantages, are summarised in Table 1. However, these studies have focused on only one MPLS technology, whereas our review paper addresses broad-ranging MPLS technologies, including traffic engineering (TE), differentiated services (DiffServ), protection and restoration, and MPLS-transport profile (MPLS-TP, an MPLS protocol), in addition to its applications and recent issues.…”

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

“…The key difference between mdx and these projects is that they focus only on offering computing resources and associated software and services, whereas the mdx platform provides not only such resources, software, and services but also seamless and secure connection to data sources such as IoT devices and scientific facilities. To achieve this, the mdx network is designed to enhance the connectivity of SINET [15], which is an ultra high-speed/low-latency academic backbone network throughout Japan.…”

Practical guide for managing large-scale human genome data in research

“…The security and legal compliance for the personal genome analysis environment of the NIG supercomputer is supervised by the National Bioscience Database Center (NBDC) in the Japan Science and Technology Agency (JST), and the NIG supercomputer is designated as available server outside of the affiliated organization ("Off-premise-server") in Japan (https://humandbs.biosciencedbc.jp/ en/off-premise-server). The system is connected to the commercial cloud vendors including AWS via the SINET5 network system hosted by NII, Japan [53], and on this platform, we have developed a multi-cloud infrastructure with the cooperation among National Institute of Information, Hokkaido University, Tokyo Institute of Technology, Kyushu University, and National Institute of Genetics.…”