The University of Tokyo / System Innovation, Graduate School of Engineering
Engineering Traffic Uncertainty in the OpenFlow Data Plane
The purpose of Research:We were driven by a simple question of whether traffic engineering in Software Defined Networking (SDN) can react quickly to bursty and unpredictable changes in traffic demand. The key challenge is to strike a careful balance between the overhead (frequently involving the SDN controller) and performance (the degree of congestion measured as the maximum load and the balance between the minimum and the maximum loads). Exploiting OpenFlow (OF) features, a quick shift of routing paths for unpredictable traffic bursty is the focal point of this work. Process of Research: 1.Establish the traffic model, do the investment about the recent progress about network structure and algorithms. Decide our new model of traffic delivery.(Primary working part of Dr.Chen Fei) 2.Evaluate the algorithm, and simultaneously determine the simulation model, create a simulation environment.(My primary working part) 4.We designed the simulation experiment through simulator I developed, during this process we adjusted models and algorithms, evaluated accuracy and adequacy of the model. 5. Write the paper, and finally, our paper was luckily accepted by IEEE INFOCOM conference, April 2016 Research Result:Our purpose was achieved by using a dual routing scheme and letting the data plane to select the appropriate path in reacting to uncertainty in traffic load. The proposed work is called DUCE (Demand Uncertainty Configuration selection). Further, we describe a traffic distribution model, an optimization solution that calculates congestion-free traffic distribution plan which guarantees that each switch can select one of the paths in a distributed way, and moreover, of details about detaching the functionality of responding to the demand uncertainty from the control plane and delegating it to the data plane. Simulations are performed validating the efficiency of DUCE under various network scenarios.