A recursive approach to network management

Nowadays there is an increasing need for a general management paradigm which can simplify network management and further enable network innovations. In this paper, in response to limitations of current Software Defined Networking (SDN) management solutions, we propose a recursive approach to enterprise network management, where network management is done through managing various Virtual Transport Networks (VTNs). Different from the traditional virtual network model which mainly focuses on routing/tunneling, our VTN provides communication service with explicit Quality-of-Service (QoS) support for applications via transport flows, and it involves all mechanisms (e:g:, routing, addressing, error and flow control, resource allocation) needed to support such transport flows. Based on this approach, we design and implement a management layer, which recurses the same VTN-based management mechanism for enterprise network management. Comparing with an SDN-based management approach, our experimental results show that our management layer achieves better network performance

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall