A genetic algorithm‐based flow update scheduler for software‐defined networks

• Published in: International journal of communication systems Vol. 33; no. 2
• Main Authors: Abbasi, Mohammad Reza, Guleria, Ajay, Devi, Mandalika Syamala
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.01.2020
• Subjects: Completion time; Configurations; Controllers; Electronic devices; Genetic algorithms; network update; Overflow; Production scheduling; Schedules; Scheduling; Software-defined networking; software‐defined networks; Solution space; Switches; Switching theory; Traffic control; Traffic engineering; Upgrading
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4188

Summary Software‐defined networking (SDN) facilitates network programmability through a central controller. It dynamically modifies the network configuration to adapt to the changes in the network. In SDN, the controller updates the network configuration through flow updates, ie, installing the flow rules in network devices. However, during the network update, improper scheduling of flow updates can lead to a number of problems including overflowing of the switch flow table memory and the link bandwidth. Another challenge is minimizing the network update completion time during large‐network updates triggered by events such as traffic engineering path updates. The existing centralized approaches do not search the solution space for flow update schedules with optimal completion time. We proposed a hybrid genetic algorithm‐based flow update scheduling method (the GA‐Flow Scheduler). By searching the solution space, the GA‐Flow Scheduler attempts to minimize the completion time of the network update without overflowing the flow table memory of the switches and the link bandwidth. It can be used in combination with other existing flow scheduling methods to improve the network performance and reduce the flow update completion time. In this paper, the GA‐Flow Scheduler is combined with a stand‐alone method called the three‐step method. Through large‐scale experiments, we show that the proposed hybrid approach could reduce the network update time and packet loss. It is concluded that the proposed GA‐Flow Scheduler provides improved performance over the stand‐alone three‐step method. Also, it handles the above‐mentioned network update problems in SDN. A hybrid genetic algorithm‐based flow update scheduling method is proposed. It attempts to minimize the completion time of the network update without overflowing the flow table memory of the switches and the link bandwidth. The experimental results show that the proposed GA‐Flow Scheduler can improve the performance of SDN‐based networks.