Changes between Version 42 and Version 43 of GEC23Agenda/EveningDemoSession

05/26/15 16:06:40 (5 years ago)



  • GEC23Agenda/EveningDemoSession

    v42 v43  
    372372 Attendees interested in Microgrid, smart grid, and Software Defined Networking should attend demo. Microgrid is an emerging and promising paradigm to improve the resilience of the electric distribution infrastructure. The communication infrastructure plays a particularly critical role for microgrids with renewable energy sources due to their much smaller inertia as compared to traditional energy generation sources. The poster shows our current work on using ultra-fast programmable networks as the communication infrastructure for microgrids. Specifically, we show various functionalities including route reconfiguration, packet prioritization and guaranteed latency, realized using a local testbed and Open vSwitches in GENI infrastructure.
     376<h1 style="text-align: center; color: #FF0000">
     377<div class="alignleft" style="width:100%;height:2;border-top:2px solid #FF0000;"></div>
     380==== Resilience of KanREN !OpenFlow network to large-scale disasters ====
     381'' This demo shows the response of the Kansas KanREN network to large-scale disasters using a GUI to draw the area under the disaster.''
     383Attendees interested in network resilience and !OpenFlow should attend. Our demo is an interactive visualization system that shows how the KanREN OpenFlow network behaves in the presence of area-based challenges. Our visualization system consists of a Google Map front-end and a server to communicate the events between the front-end and the challenged network. The challenges can be determined by the user using a real-time editable polygon. Furthermore, the visualization system shows real-time performance parameters of the challenged network. When the challenge is applied on the map, the nodes in the polygon will be removed from the underlying OpenFlow network topology. Two controllers are running to monitoring the network and manage the packet forwarding; one is the layer 2 learning switch controller with emulated convergence time, while the other running our geographical routing protocol — GeoDivRP. We present the real-time packet delivery ratio for both of the controllers and demonstrate the effectiveness of our routing mechanism in SDN environment.
     387  * James Sterbenz,, The University of Kansas