Analyzing the Impact of Granularity on IP-to-AS Mapping Presented by Baobao Zhang Authours: Baobao...
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Transcript of Analyzing the Impact of Granularity on IP-to-AS Mapping Presented by Baobao Zhang Authours: Baobao...
Analyzing the Impact of Granularity on IP-to-AS Mapping
Presented by Baobao ZhangAuthours:Baobao Zhang, Jun Bi, Yangyang Wang, Jianping Wu
1 Introduction
Doing? Map the IP address to the AS that uses the IP
Meaning Help network managers diagnose network
failure Discover the AS-level topology with
traceroute Some other applications that need to map IP
to AS
An example
2 Data Collection
Data Source Traceroute Data (From CAIDA) BGP Routing Table (from routeviews)
Processing into pairs Extract the prefixes and AS paths from routing tables Extract the destination IPs and IP paths from traceroute
data Find the longest matching prefix for the destination IP The IP path associated with the destination IP and the AS
path associated with the longest prefix form one pair Origin IP-to-AS mapping
Extract the prefixes and its origin ASes from routing tables Map every prefix to its origin AS
Data Collection
Date: 04/22/2010 During: One Day
3 Methodology
Definition Exact Match Ambiguous Match Mismatch
Methods Prefix-granularity Method (PGM) IP-granularity Method (IGM) Prefix-granularity Limit Method (PGLM) Hierarchical Mapping System (HMS)
Assumption The traceroute path is consistent with the BGP AS
path.
Methods
Prefix-granularity Method (PGM) i.e. Mao’s Method Bind many IP addresses into one prefix Map one prefix to many ASes by setting threshold Tight coupling
Pros Can modify the incorrect mappings for the IPs that don’t
appear in the training dataset Cons
Mistakenly modify the originally correct mappings for the IPs that don’t appear in the training dataset. (tight coupling)
Threshold. Miss to modify the incorrect mappings for the IPs that appear in the training dataset
Threshold. Bring about ambiguous mappings
Methods
IP-granularity Method (IGM) We propose it for the first time Map one IP to one only AS Loose coupling
Pros Eliminate the ambiguous mappings
Cons Only can modify the mappings for the IPs
that appear in the training dataset.
Methods
Prefix-granularity Limit Method (PGLM) One fictitious Method The Limit of PGM. Set the threshold
=0 It is only used to be compared
Methods
Hierarchical Mapping System (HMS) Combine the IGM with PGM Three levels (/32 level, /24 level, origin level) Firstly look up in the /32 level mapping, then /24
level mapping, finally the origin level mapping Pros
complement the strength of tight coupling and loose coupling
Cons * inherit the characteristic of ambiguity from
PGM
4 Evaluation
DataSet
Evaluation
Training Accuracy
Evaluation
Validation Accuracy
Evaluation
Compare trained mapping with the origin mapping
Evaluation
5 Classification Tree Analysis
Motivation Quantify the pros and cons for the
IGM and PGM Analyze the obstacles in the way of
improving the accuracy for the IGM and PGM
Other potential findings
Constructing Classification Tree
Table 7 The improvement gained by correcting the mapping of
the types for the PGM VDS1gain
VDS2gain
VDS3gain
VDS4gain
Type1 0.00% 0.00% 0.00% 0.00%
Type2 0.71% 0.02% 0.27% 0.05%
Type3 14.25% 8.47% 8.15% 10.30%
Type4 0.00% 0.00% 0.00% 0.00%
Type5 2.37% 1.55% 0.35% 2.47%
Type6 0.00% 0.00% 0.00% 0.00%
Type7 0.80% 1.57% 1.47% 1.05%
Type8(Base)
-0.29%(5.66%)
-0.64%(7.34%)
-0.15%(6.79%)
-0.33%(6.20%)
Type1-2(Base)
0.00%(1.06%)
0.00%(0.61%)
0.00%(0.58%)
0.00%(1.92%)
Type2-2 0.36% 0.06% 1.01% 0.25%
Type3-2 0.42% 1.12% 22.29% 15.08%
Type4-2 0.00% 0.00% 0.00% 0.00%
Type5-2 0.45% 0.17% 0.25% 3.30%
Type8-2(Base)
0.00%(2.93%)
0.00%(2.38%)
-0.03%(2.22%)
-0.01%(0.15%)
Type-all 19.85% 12.87% 35.18% 32.94%
5.1 Quantify the pros and cons for the IGM and PGM
Pros and Cons (+) modify the incorrect mappings for the IPs that don’t
appear in the training dataset (Type 8-2, 1-2 for PGM, nothing for IGM)
(-) Mistakenly modifies the originally correct mappings for the IPs that don’t appear in the training dataset. (Type 2-2 for PGM , nothing for IGM)
(-) Miss to modify the incorrect mappings for the IPs that appear in the training dataset (Type3 for PGM and IGM)
Quantifying For PGM, Base(type8-2)+base(type1-2)-gain(type2-2) is
positive. 3.63%, 2.93%, 1.79% and 1.81% PGM(gain(type3))-IGM(gain(type3)) . 14.00%, 8.38%, 7.94% and 9.81%
Conclusion The IGM is superior to the PGM
5.2 Analyze the obstacles in the way of improving the accuracy for the IGM and PGM
IGM Type 7. (IPs do not appear in the
training dataset) PGM
Type 3. (IPs appear in the training dataset, but miss to modify due to the tight coupling)
Type 3-2. (IPs do not appear in the training dataset)
5.3 Other findings
The limit of validation accuracy1-gain(type2) -gain(type3)-gain(type5)
For IGM98.87%,97.96%,98.43% ,98.96%
For PGM82.66%,89.96%,91.23% ,87.18%
Other findings
Illustrating that the IGM has more potential to
improve the accuracy than the PGM
6 Conclusion
Proposed a hierarchical IP-to-AS mapping system
Analyzed and quantified the impact of granularity