Is your solution accurate? A fault-oriented performance prediction method for enhancing communication network reliability

Communication network performance prediction is a basic tool for designing robust networks, and its precision directly determines the accuracy of downstream task solutions. Modern communication networks possess adaptability, which recovers from faults by updating routing tables and adjusting flow pa...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Reliability engineering & system safety Ročník 256; s. 110793
Hlavní autoři: Yang, Fang, Ma, Tao, Shu, Nina, Liu, Chunsheng, Wu, Tao, Chang, Chao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.04.2025
Témata:
Dynamic queuing network
Network adaptive behavioral dynamics
Network fault
Network performance prediction
Network fault
Dynamic queuing network
Network adaptive behavioral dynamics
Network performance prediction
ISSN:0951-8320
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Communication network performance prediction is a basic tool for designing robust networks, and its precision directly determines the accuracy of downstream task solutions. Modern communication networks possess adaptability, which recovers from faults by updating routing tables and adjusting flow paths, etc. However, the existing performance prediction methods do not consider the complex adaptive behavior of the network post-fault, leading to significant prediction errors. In this paper, we propose a fault-oriented communication network performance prediction method based on network adaptive behavioral dynamics and dynamic queuing networks (NAB-DQN). This method models the dynamic behavior of the network after faults from the routing perspective and transforms them into M/M/1/K capacity-limited dynamic Jackson open network. Finally, NAB-DQN implements flow-level network performance prediction based on flow path dependency characteristics. We validate the accuracy, generalizability, and efficiency of the algorithm through extensive experiments from different network scenarios (e.g., topology, Flow Density, routing protocol, network size) and fault scenarios (e.g., router, link, port). The experimental results show that NAB-DQN achieves less than 10% error in most scenarios compared to the Discrete Event Simulator (DES) and achieves more than two data orders of magnitude speedup.
ISSN:0951-8320
DOI:10.1016/j.ress.2024.110793