Network topology generation based on eigenvector centrality with real‐time guarantee

Abstract

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case.

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

“…For example, for an RL=2 network, each ES has two fixed access switches. As for the choice of access switches, we partly refer to the strategy in [16], which is to select access switches based on the deployment location of the ESs in the vehicle (optimization can also be done based on reducing the total communication volume between the core network).…”

“…He et al. [16] proposed a generic topology generation algorithm that combines node degree, eigenvector centrality, and real‐time guarantees to optimize AFDX networking. However, the redundancy strategies that use FNLR are not scalable in terms of weight, space, and resource efficiency [17–19].…”

“…For example, Annighoefer et al [15] proposed a combinatorial optimization approach to compute a multi-objective Pareto-optimal solution for network topology optimization and demonstrated the optimization in the context of A320 and A380 scenarios. He et al [16] proposed a generic topology generation algorithm that combines node degree, eigenvector centrality, and realtime guarantees to optimize AFDX networking. However, the redundancy strategies that use FNLR are not scalable in terms of weight, space, and resource efficiency [17][18][19].…”