摘要
The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.
基金
the China Postdoctoral Science Foundation(2023TQ0089)
the National Natural Science Foundation of China(Nos.62072465,62172155)
the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
