Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

Abstract

Software Defined Networks (SDN) introduced better network management by decoupling control and data plane. However, communication reliability is the desired property in computer networks. The frequency of communication link failure degrades network performance, and service disruptions are likely to occur. Emerging network applications, such as delay-sensitive applications, suffer packet loss with higher Round Trip Time (RTT). Several failure recovery schemes have been proposed to address link failure recovery issues in SDN. However, these schemes have various weaknesses, which may not always guarantee service availability. Communication paths differ in their roles; some paths are critical because of the higher frequency usage. Other paths frequently share links between primary and backup. Rerouting the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system throughput. Therefore, there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link failure. This paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT, increasing network throughput while minimizing post-recovery congestion. The affected flows are redirected through a path with minimal risk of failure, while Bayesian probability is used to predict post-recovery congestion. Both the former and latter path with a minimal score is chosen. The simulation results improved throughput by (45%), reduced packet losses (87%), and lowered RTT (89%) compared to benchmarking works.