AS relationships, customer cones, and validation

Abstract

Business relationships between ASes in the Internet are typically confidential, yet knowledge of them is essential to understand many aspects of Internet structure, performance, dynamics, and evolution. We present a new algorithm to infer these relationships using BGP paths. Unlike previous approaches, our algorithm does not assume the presence (or seek to maximize the number) of valley-free paths, instead relying on three assumptions about the Internet's inter-domain structure: (1) an AS enters into a provider relationship to become globally reachable; and (2) there exists a peering clique of ASes at the top of the hierarchy, and (3) there is no cycle of p2c links. We assemble the largest source of validation data for AS-relationship inferences to date, validating 34.6% of our 126,082 c2p and p2p inferences to be 99.6% and 98.7% accurate, respectively. Using these inferred relationships, we evaluate three algorithms for inferring each AS's customer cone, defined as the set of ASes an AS can reach using customer links. We demonstrate the utility of our algorithms for studying the rise and fall of large transit providers over the last fifteen years, including recent claims about the flattening of the AS-level topology and the decreasing influence of "tier-1" ASes on the global Internet.

Mapping the expansion of Google's serving infrastructure

“…We check this intuition by comparing the location in the AS hierarchy of clients and the servers to which Google directs them. Of clients directed to servers outside of Google's network, 93% are located within the server's AS's customer cone (the AS itself, its customers, their customers, and so on) [20]. Since correctly inferring AS business relationship is known to be a hard problem [10], it is unclear whether the remaining 7% of clients are actually served by ISPs of which they are not customers, or (perhaps more likely) whether they represent limitations of the analysis.…”

Measuring and Characterizing IPv6 Router Availability

“…We solicited validation data from operators of 12 ASes who had previously provided feedback on our AS relationship inferences [17]. Five replied with evidence that supported our inferences of reboot events for 15 routers, either direct validation from system logs, or implicit validation by correlating the event with a BGP session closing.…”

Using peeringDB to understand the peering ecosystem

“…We use CAIDA's AS-relationship algorithm [11] to infer the number of customers of each AS. We use this BGP data to determine the size of the address space that each AS originates (removing double-counting due to ASes advertising overlapping prefixes).…”

“…cone [11]. We find that 93% of the top-100, 80% of the top-200 and 74% of the top-300 ASes from AS-rank were present in the Aug13-PDB dataset, including all known Tier-1 [13] and major Tier-2 ASes [14].…”