Traffic engineering has attracted much research attention, especially in recent years as networks grow in size and complexity. Network operators increasingly need better ways to manage the massive amounts of data flowing through their networks. A group of researchers has proposed an intelligent routing scheme for traffic engineering to achieve load balancing with limited control cost.
Their research is published in the journal Mining and analyzing big data.
Traditionally, researchers have studied traffic techniques related to traditional internet protocol (IP) networks, with a focus on IP routing protocols, routing optimization problemand overlays in IP networks. With the emergence of software-defined networks, researchers began to focus more on traffic engineering issues, including traffic segmentation and protocol design.
In software-defined networks, the network can be centrally controlled using software application. Software-defined networks allow researchers to manage networks more efficiently, solving some of the high-traffic engineering problems that are difficult to manage in traditional networks.
However, even with software-defined networks, researchers still struggle with scalability issues. So the researchers turned their attention to segmental routing. Segment routing is a technique that allows researchers to simplify traffic engineering across network domains by organizing sets of information called packets.
The researchers realized that by combining segmentation routing with software-defined networks, they could address some of the challenges in software-defined networks. However, there are still some unresolved problems because segment routing carries with it the cost of control, i.e., extra packet headers must be inserted. The overhead significantly reduces the efficiency of a large network when the segment headers become too long.
“Shard routing is a new architecture for traffic engineering, but it also brings the cost of control and reduces forwarding efficiency. So we focused on how to optimize balanced performance. link load with limited control cost based on shard routing,” said Laizhong Cui, an expert. Professor of the School of Computer Science and Software Engineering at Shenzhen University.
To overcome these challenges, the research team proposed an intelligent routing scheme for traffic engineering. This allows for load balancing at the cost of limited control. The team begins by formulating the problem as a mapping problem that maps different flows to key diversion points. Next, they prove that the problem is a difficult undetermined polynomial, a way of defining the problem in computational complexity theory.
Then to solve the problem, they developed an improved ant colony optimization solution algorithm. Ant colony optimization is a technique that uses probability to solve network optimization problems. They also designed a second algorithm, the load balancing algorithm, and they analyzed its theoretical performance.
“We have proposed two algorithms to achieve the goal of load balancing and avoiding overload in transit. The theory of ant colony optimization and linear programming provided the idea,” said Laizhong Cui. and direction for algorithms”.
The team evaluated their smart routing scheme for traffic engineering in various real-world topologies. Topology describes how the elements of a network are arranged and connected. The team’s results show that their algorithm outperforms the traditional algorithm. With the intelligent routing scheme for traffic engineering, the maximum bandwidth is 24.6% lower than that of traditional algorithms, when evaluated on the Bell Canada network topology.
Looking forward to future research, the team is preparing to test and optimize their algorithms in a real network environment. They also plan to further develop their plan by adding an artificial intelligence approach to software-defined wide area networks. “Our ultimate goal is to develop and apply our solutions to most network architectures to improve network transmission performance,” Cui said.
Shu Yang et al, Smart Segment Routing: Towards load balancing with limited control costs, Mining and analyzing big data (2022). DOI: 10.26599/BDMA.2022.9020018
Provided by Tsinghua University Press
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