جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://dl.pgu.ac.ir/handle/Hannan/82078
Title: Big data for autonomic intercontinental overlays
Keywords: Science & Technology;Technology;Engineering, Electrical & Electronic;Telecommunications;Engineering;The Internet;big data;network quality of service (QoS);smart overlays;random neural network;cognitive packet network;COGNITIVE PACKET NETWORK;RANDOM NEURAL-NETWORKS;Networking & Telecommunications; Electrical And Electronic Engineering;1005 Communications Technologies;0805 Distributed Computing
Issue Date: 16-Mar-2017
3-Feb-2016
10-Jan-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Description: This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source-destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 ?? 106 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently.
URI: http://dx.doi.org/10.1109/JSAC.2016.2525518
Other Identifiers: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000372836900010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
1558-0008
http://hdl.handle.net/10044/1/44763
Type Of Material: Other
Appears in Collections:Faculty of Engineering

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