Intelligent OpenFlow Switch for SDN Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach

Abstract

The COVID-19 outbreak has impacted network operators and data centers in terms of congestion and high traffic that lead to outages and significant pressure on the network. The overhead traffic is generated from web, voice calls, and Internet activity. In this paper, we are investigating data center congestion control for Software Defined Networks (SDN) network data centers. A Software-Defined (SDN) data center is an emerging networking paradigm that simplifies the network architecture by decentralizing plane functionality into a single with centralized decision capabilities. Along with the SDN paradigm, there is a crucial part that is responsible for forwarding packet called OpenFlow switching engine. In a typical SDN environment, the rules are initiated by the SDN controller and pushed to the OpenFlow switches. The traditional OpenFlow switch has no forwarding decision and depends on the incoming policies from the controller’s southbound interface. Additionally, the flow of traffic is initiated from different sources that are assigned to a specific route. However, this significant flow of traffic due to COVID-19 can lead to congestion and degradation of network performance in terms of delay and interruption. To be precise, a single OpenFlow switch could receive a capacity of traffic that floods its forwarding table and lead to link flaps and outages. In order to optimize the OpenFlow switch with regards to how much traffic it can host and to adjust routing capabilities for dynamic changes in the network, we propose an optimized OpenFlow congestion control and fault prediction framework for inbound traffic to overcome the inefficient route planning in the network. The proposed developed optimization algorithm is based on Genetic Evolutionary Algorithm criteria and adds intelligence to the OpenFlow switch by the adoption of Fuzzy Logic prediction capabilities. The experimental evaluation shows that the proposed optimization method adds significant intelligence and optimization to OpenFlow operation. The testbed was implemented experimentally using Raspberry Pi (RPI)cluster with customized SDN and OpenFlow deployment. The probability of the best fitness was 14.11% for Gen 999. The proposed approach adds intelligence and prediction into the OpenFlow switch to overcome the unstable flows of traffic and to predict faults to enhance the traffic capacity levels and manage flows into an entirely uninterrupted production environment.