topology modeling via cluster graphs balachander krishnamurthy and jia wang at&t labs research
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Topology Modeling via Cluster Graphs
Balachander Krishnamurthy and Jia Wang
AT&T Labs Research
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Internet Topology graphs
Understand Internet topology Traffic patterns Protocol design Performance evaluation
Two levels of granularity Inter-domain level – AS graphs Router level – router graphs
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AS graphs
Construction: AS-Path-based: BGP routing tables or
update messages Traceroute-based Synthetic: power laws
Pros and cons Coarse-grained Easy to generate Incomplete Connectivity reachability
AS graphs are too coarse-grained!
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Router graphs
Construction Traceroute-like probing Interface collapsing algorithms
Proc and cons Very fine-grained Expensive
Router graphs are too fine-grained!
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Network-aware clusters
Obtain BGP tables from many places via a script and unify them into on big prefix tableExtract IP addresses from logsPerform longest prefix matching on each IP addressClassify all the IP addresses that have the same longest matched prefix into a cluster (identified by the shared prefix)
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Cluster graphs
Intermediate-level of granularityUndirected graph Node: cluster of routers and hosts Edge: inter-cluster connection
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Cluster graphs
Construction Hierarchical
graphs Traceroute-based
graphs Synthetic graphs
•Extend AS graph by modeling the size/weight of AS•Use cluster-AS mapping extracted from BGP tables
•Traceroute to sampled IPs in interesting clusters•Construct a cluster path for each sampled IP•Merge cluster paths into a cluster graph
•Based on some observed characteristics, e.g., power laws
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Super-clusteringGroup clusters into super-clusters based on their originating ASBGP tables: May 2001Web log: a large portal site in March 2001 # of requests: 104M # of unique IPs: 7.6M # of clusters: 15,789 # of busy clusters (70% of the total): 3,000 # of super-clusters: 1,250 # of super-clusters with size >1: 436 Avg size of super-clusters: 2.4
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Busy clusters in super-cluster
Cluster prefix Common name suffix
139.130.0.0/16 wnise.com
139.134.0.0/16 tmns.net.au
192.148.160.0/24 telstra.com.au
203.32.0.0/14 ocs.com.au
203.36.0.0/16 tricksoft.com.au
203.38.0.0/16 panorama.net.au
203.0.0.0/10 geelong.netlink.com.au
203.0.0.0/12 iaccess.com.au
ASes are too coarse-grained!
AS 1221
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Cluster graph
Top 99 busy clusters # unique IPs: 1.2M Sample 99 IPs (1 from each cluster)
Traceroute to 99 sampled IPs Ignore probes returning ‘*’: 17% Ignore unreachable probes(!N, !H, !P, !X):
0.3%
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Cluster path
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Cluster graph vs AS graph
Observations Cluster graph has 34% more nodes
and 15% more edges than AS graph. The average node degree in cluster
graph is 15% less than that in AS graph.
Correlation between cluster hop counts and end-to-end hop counts is stronger than that of AS hop counts.
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Cluster graph vs router graph
Observations Constructing cluster graph needs
much less traceroutes than router graph (99 vs thousands).
More traceroutes show that cluster graph is more stable than router graph.
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Comparison of three models
Model AS graph
Cluster graph
Router graph
Granularity Coarse Intermediate
Fine
Construction
Stableness Accuracy
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Conclusion
Examine Internet topology models Cluster graphCompare three modelsCluster graphs are less complicated and more stable than router graphs.Cluster graph can be obtained as easy as AS graphs while providing more fine-grained information that capture the Internet topology.