A Resilience-Oriented and NFV-Supported Scheme for Failure Detection in Software-Defined Networking

Abstract

As a recently emerging network paradigm, Software-Defined Networking (SDN) has attracted considerable attention from both industry and academia. The most significant advantage of SDN is that the paradigm disassociates the control logic (i.e., control plane) from the forwarding process (i.e., data plane), which are usually integrated into traditional network devices. Thanks to the property of centralized control, SDN enables the flexibility of dispatching flow policies to simplify network management. However, this property also makes the SDN environment vulnerable, which will cause network paralysis when the sole SDN controller runs malfunction. Although several works have been done on deploying multiple controllers to address the failure of a centralized controller, their drawbacks are leading to inefficiency and balance loss of controller utilization, provoking resource idling as well as being incapable to suffice flow outburst. Additionally, the network operators often put a great deal of effort into discovering failure nodes to recover their networks, which can be mitigated by applying failure detection before the network deterioration occurs. Network traffic prediction can serve as a practical approach to evaluate the state of the OpenFlow-based switch and consequently detect SDN node failures in advance. As far as prediction solution is concerned, most researchers investigate either statistical modeling approaches, such as Seasonal Autoregressive Integrated Moving Average (SARIMA), or Artificial Neural Network (ANN) methods, like Long Short-Term Memory (LSTM) Neural Network. Nonetheless, few of them study the model merging these two mechanisms regarding multi-step prediction. This thesis proposes a novel system associated with Network Function Virtualization (NFV) technique to enhance the resilience of SDN network. A hybrid prediction model based on the combination of SARIMA and LSTM is introduced as part of the detection module of this system, where the potential node breakdown can be readily determined so that it can implement smart prevention and fast recovery without human interaction. The results show the proposed scheme improves the performance concerning time complexity compared with that of previous work, reaching up to 95% accuracy while shortening the detection and recovery time by the new combined prediction model.