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Using random linear network coding in dynamic storage environments

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If you have a question about this talk, please contact Dr Ramji Venkataramanan.

While structured coding has been extensively considered for data repair in static networks, such approaches seem ill-suited to current clouds, which exhibit dynamic topologies and demands. We first demonstrate, through benchmarking, that random network coding can provide speeds that outperform, in terms of encoding and decoding throughput, commercial systems using structured codes. We then present two case studies. We first considers a distributed storage system with full failure or with intermittent availability of nodes. We show that random coding far outstrips, in terms of availability, uncoded duplication. This advantage is increased when content is regenerated at nodes from partial content from other nodes, in which case random linear network coding also outperforms common structured maximum-distance separable codes. The second case study illustrates how coding can be used to manage hybrid managed content distribution networks with peer-to-peer edge clouds, such as are currently being proposed to augment edge caching, when bandwidth availability may be variable.

Joint work with Hassan Charaf, Flavio du Pin Calmon, Frank Fitzek, Janus Heide, Daniel Lucani, Morten Pedersen, Martin Sipos, Aron Szabados, Tamas Toth, Peter Vingelmann, Weifei Zeng.

BIO: Muriel Médard is the Cecil H. Green Professor in the Electrical Engineering and Computer Science Department at MIT . She has served as editor for many IEEE publications and she is currently Editor in Chief of the IEEE Journal on Selected Areas in Communications She was President IEEE Information Theory Society in 2012. She has served as TPC co-chair of ISIT , WiOpt, CONEXT , and Netcod, and co-chair of ISIT and Netcod. She was awarded the 2009 Communication Society and Information Theory Society Joint Paper Award, the 2009 William R. Bennett Prize in the Field of Communications Networking, the 2002 IEEE Leon K. Kirchmayer Prize Paper Award and several conference paper awards. She was co-winner of the MIT 2004 Harold E. Edgerton Faculty Achievement Award. In 2007 she was named a Gilbreth Lecturer by the U.S. National Academy of Engineering. She received the 2013 MIT Graduate Student Council EECS Mentor Award. In 2014 she was named by Thomson Reuters one of the World’s Most Influential Scientific Minds.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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