Name
Abstract | ||
|---|---|---|
Software-Defined Networking (SDN) is an emerging networking technology that has attracted intense interest from both the industry and research communities. Thus far, it is primarily applied to datacenters and research network environments. Despite meticulous effort in planning and equipment selection prior to deployment, there remain unknowns that can affect the network's performance after equipment has been deployed and is fully operational. Network administrators and planners would benefit from a tool that is able to monitor the load on various network entities and visualize this in real-time and, even better, predict likely performance changes arising from traffic variation; this allows them to make prompt decisions to prevent seemingly small hotspots from becoming major bottlenecks. In this paper, we present a network visualization and performance prediction tool that enables network planners to examine how their networks' performance will be affected as the traffic loads and network utilization changes. This is a first of its kind where performance prediction is based on queueing analytic models of the network configuration coupled with real-time measurements taken from the network devices. |
Year | Venue | Field |
|---|---|---|
2016 | IEEE IFIP Network Operations and Management Symposium | Network processor,Computer science,Networking hardware,Computer network,Network simulation,Network architecture,Software-defined networking,Network traffic control,Network management station,Intelligent computer network,Distributed computing |
DocType | ISSN | Citations |
Conference | 1542-1201 | 1 |
PageRank | References | Authors |
0.38 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jordan Ansell | 1 | 1 | 0.72 |
| Winston K. G. Seah | 2 | 453 | 39.78 |
| Bryan Ng | 3 | 100 | 20.84 |
| Stuart Marshall | 4 | 301 | 23.77 |