Making queueing theory more palatable to SDN/OpenFlow-based network practitioners

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.

[1]  Wei Zhou,et al.  Evaluating the controller capacity in software defined networking , 2014, 2014 23rd International Conference on Computer Communication and Networks (ICCCN).

[2]  Olav N. Østerbø,et al.  Modelling of OpenFlow-based software-defined networks: the multiple node case , 2015, IET Networks.

[3]  Richard Wolski,et al.  Forecasting network performance to support dynamic scheduling using the network weather service , 1997, Proceedings. The Sixth IEEE International Symposium on High Performance Distributed Computing (Cat. No.97TB100183).

[4]  Jeffrey D. Case,et al.  Simple Network Management Protocol (SNMP) , 1989, RFC.

[5]  Qiang Xu,et al.  PROTEUS: network performance forecast for real-time, interactive mobile applications , 2013, MobiSys '13.

[6]  Thierry Turletti,et al.  A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks , 2014, IEEE Communications Surveys & Tutorials.

[7]  Bryan Ng,et al.  Queueing Analysis of Software Defined Network with Realistic OpenFlow–Based Switch Model , 2016, 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS).

[8]  Alex C. Snoeren,et al.  Challenges in the emulation of large scale software defined networks , 2013, APSys.

[9]  Jim Esch,et al.  Software-Defined Networking: A Comprehensive Survey , 2015, Proc. IEEE.

[10]  Paul Goransson,et al.  The OpenFlow Specification , 2014 .

[11]  Simon Oechsner,et al.  Modeling and performance evaluation of an OpenFlow architecture , 2011, 2011 23rd International Teletraffic Congress (ITC).

[12]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[13]  Nick McKeown,et al.  Reproducible network experiments using container-based emulation , 2012, CoNEXT '12.

[14]  Wolfgang Kellerer,et al.  Interfaces, attributes, and use cases: A compass for SDN , 2014, IEEE Communications Magazine.

[15]  M. Thomas Queueing Systems. Volume 1: Theory (Leonard Kleinrock) , 1976 .

[16]  Chuang Lin,et al.  Scalability of control planes for Software defined networks: Modeling and evaluation , 2014, 2014 IEEE 22nd International Symposium of Quality of Service (IWQoS).