# Layer 1-Informed Internet Topology Measurement

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9 Fraction of POPs discovered relative to Internet Atlas VP in,t in VP in,t out VP out,t in Abiline Bandcon Abovenet B.T.N. CRL. Cwix E.L. DataXchange Epoch Goodnet Highwinds H.E. Integra INSIXC Napnet Layer42 Noel NSFnet Oxford Peer1 RedBestel RedIris Shentel USSignal Spiralight Internet Service Provider (ISP) Figure 2: Number of POPs discovered by different probing modalities. servers, traceroute servers, telecom hotels, and other entities that may simply respond to probes. From this set, we subselect the destinations such that the square of the Euclidean distance between the VP and destination is greater than the sum of the squares of the distance between VP and target and VP and destination. This has the effect of creating a measurement cone centered at the VP and directed toward the target node (step 6). These destinations are then iteratively probed using traceroute. For each completed trace we determine whether the target has been found using location hints. If it has, the algorithm completes. If not, we continue until we have exhausted all VPs and their corresponding destination sets. Figure 3 depicts the targeting process of POPsicle. Algorithm 1: POPsicle algorithm input: targetnet = target network input: L T = list of targets to be identified input: L S vp = list of source VPs with known coordinates // Scan target network to find reachable hosts 1 scanresults = scan(targetnet); 2 L D vp = inferlocations(scanresults); 3 foreach t in L T do // Choose destination VPs that are closest to reachable hosts 4 S t vp = geographicallynearest(t, L S vp); 5 foreach vp in S t vp do // Greedily choose probing destinations within a cone extending from vp to t 6 D t vp = searchcone(vp, t); 7 foreach dst in D t vp do 8 send probe from vp to dst; 9 if t found then 10 record success for t; 11 goto step 3; Figure 3: POPsicle targeting process. VPs within the ISP that are geographically closest to the target are selected along with destinations that are geographically closest to the target and on the other side of the VP. POPsicle is based on the notion that target POPs will be part of routes that connect sources and destinations located on either side (from a Euclidean perspective) of the target. We argue that this is likely due to shortest path intra-domain routing. POPsicle is also currently dependent on location hints from DNS for both destination identification and to identify when a target has been discovered. IP geolocation could be used to address the former, while the latter could be addressed through publicly available data (e.g., PeeringDB) by associating IP address ranges with POP locations. 7.2 POPsicle Evaluation We selected 30 looking glass servers from the Atlas database that satisfied the following criteria: (1) the server is co-located with an IXP in North America, and (2) the ground truth information of the POPs is available either from Internet Atlas or in PeeringDB [5]. The vast majority of providers we selected for analysis are regional providers since we found them to be poorly represented in Ark probing results and thus prime candidates for detailed study. In terms of the number of networks used in this study, the coverage of our technique can appear limited. We had to remove several networks from our study due either to the incompleteness of the physical or network maps, or due to the lack of DNS locations hints. The selection of these 30 looking glass servers resulted in 13 service provider networks that were the focus for our evaluation. We began by examining Internet2, which we consider a special case since complete ground truth for all the layer1, layer2, and layer3 devices is available [2]. POPsicledirected probing found 10 out of the 10 POPs in Internet2 that house layer 3 infrastructure. We initiated probing on the remaining set of 12 ISPs using POPsicle-directed probing to verify and map the POPs for 389

11 Table 4: Summary of results from mapping infrastructural nodes. POPs (for 13 ISPs) Datacenters DNS Servers NTP Servers IXPs Total locations POPsicle Ark Atlas POPsicle compared to Atlas 61.07% 75.98% 69.23% 75.82% 56.92% 73.13% Ark compared to Atlas 54.60% 49.14% 7.69% 6.65% 38.46% 30.11% Improvement 1.04x 1.54x 9x 11.40x 1.48x 2.42x them in this initial study. Also, we note that the ISPs we considered in our POPsicle deployment have, unfortunately, little overlap with the ISPs we consider in Section 5 (and which appear in Table 3) due to our requirement that we have ground truth and location hint information available only Hurricane Electric and HopOne are in common. Table 5 shows the results of our IXP-based POPsicle deployment. We observe from the table that POPsicle finds all nodes for 8 out of the 10 ISPs (as compared with the Atlas physical topology data). In the Ark measurements, 6 out of 10 ISPs are fully mapped. For the two ISPs the POPsicle is not able to fully map, a very likely possibility is that the unobserved POPs are invisible to layer3 probes due to configured router policies [31], thus the results we show may be the best that can be achieved through active probing. Overall, our results suggest that POPsicle could be deployed more broadly to accurately map (to the extent possible) ISPs for which we do not have ground truth. Special Cases. (1) The number of POPs found for HE in Table 3 is 23 but in Table 5 the number of POPs found for HE is 24. That is, POPsicle deployed at Equinix Chicago saw an extra node in Calgary, Canada (YYC) which is verified with Datahive IX. One possible implication of this result is that such probe-based measurements are biased towards the vantage points selected. (2) For 2 ISPs (PaeTec and Atlantic Metro) some POPs were not visible to our probes, which we intend to investigate further in future work. There is anecdotal evidence that ISPs typically do not expose certain locations to traceroute probes (or any access methods from outside) even when layer 3 services are available at that particular location due to security reasons [31]. Case Study: Cogent. In [21], Ferguson et al. present an analysis of Cogent Communication s network based on using reverse DNS records, as well as location-based naming hints. We used the dataset made public by these authors to evaluate, compare and validate POPsicle s probe-based measurement of Cogent s network. We processed the DNS names from Ferguson et al. s dataset using the modified version of location inference technique developed by Chabarek et al. [12] and identified 187 POP locations. We then used POPsicle deployed at the Equinix Chicago IXP to target routers within Cogent s network, and it identified 173 POPs. In Appendix A, we see that there are 186 POP locations identified in the Atlas physical topology; it is likely that the additional POP identified in the Ferguson et al. dataset is a more recent deployment than was found in Atlas. Also in the table of Appendix A, we see that there are 122 POPs identified through the Ark probes. Lastly, we note that in the most recent Rocketfuel data, there are only 45 POP locations identified. Altogether, these results show that POPsicle s probing technique is very effective for discovering locations of physical infrastructure like POPs, is much better than existing probe-based techniques, and nearly as good as exhaustive use of reverse DNS records. Table 5: Summary results of network POPs identified with POPsicle deployed at the Equinix Chicago IXP. ISP Name POPsicle Atlas Ark BTN HE Internet PaeTec Nexicom HopOne Indiana Gigapop MOREnet Atlantic Metro Steadfast.net Main findings and implications We describe a new targeted probing technique called POPsicle that is designed to reveal and confirm the presence and location of physical infrastructure such as POPs. To evaluate our method, we used publicly accessible looking glass servers deployed at IXPs, and made a custom deployment of POPsicle at the Equinix Chicago IXP. POPsicle finds 2.4x more physical nodes than Ark probes, and in our custom deployment in Chicago, POPsicle finds nearly all POPs identified in the Atlas physical topologies. In a case study of Cogent s network, POPsicle identified more than 90% of the POPs known through Atlas as well as through the recently described technique based on using reverse DNS records [21]. Moreover, it found many more POPs than Ark probes, or the most recent Rocketfuel measurements. Overall, our results show that an IXP deployment provides a prime location from which to launch targeted topology discovery probes. Since Rocketfuel maps are commonly used in networking studies that require realistic and representative network topologies, we view this deployment paradigm as having significant potential for generating machine-readable topological information on an on-going basis. We plan to investigate the possibility for additional IXP deployments and a full-fledged system for generating up-to-date network topology data in future work. The peering model in which IXPs operate is different across different continents. For instance, an IXP in Europe is completely different from an IXP in North America. On one hand the peering model in North America typically involves a commercial colo-operator who also operates the peering equipment. On the other hand, the exchange points in Europe tend to be non-profit, community-based organizations, and the colocation and peering equipment operators 391

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14 APPENDIX A. COMPARISON RESULTS Table 6: Summary comparison of nodes and links observed in physical and network-layer topologies for networks with a footprint in North America. Physical Network-layer Nodes Links ISP Nodes Links Nodes Links Intersection Only Only Intersection Only Only N Index in P in N in P in N AT&T Cogent NTT Tinet Sprint Level Tata Abiline Ans ATMnet Bandcon BBNPlanet BellCanada BellSouth BTNorthAmerica CompuServe DarkStrand DataXchange Digex Epoch Getnet Globalcenter Gridnet HiberniaCanada HiberniaUS Highwinds HostwayIntl HE Integra Intellifiber Iris Istar Layer Napnet Navigata Netrail NetworkUSA Noel NSFnet Ntelos Oxford PacketExchange Palmetto Peer RedBestel Syringa USSignal VisionNet Xeex Xspedius

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