Algorithmic Problems Related to Internet Graphs · ToR-Problem Given: undirected graph G, set P of...
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Algorithmic ProblemsRelated to Internet Graphs
Thomas Erlebach
Based on joint work with:Zuzana Beerliova, Pino Di Battista, Felix Eberhard,Alexander Hall, Michael Hoffmann, Matúš Mihal’ák,
Alessandro Panconesi, Maurizio Patrignani,Maurizio Pizzonia, L. Shankar Ram, Thomas Schank,
Danica VukadinovicT. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.1/55
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The Internet
Size of the Internet (as of 2003):∼ 7–10M routers∼ 170M hosts∼ 650M users
In recent years, significant interest in mapping theInternet.
Different kinds of Internet graphs:Router-level graph (routers and hosts)traceroute experimentsAS-level graph (autonomous systems)traceroute, BGP tables, registriesWWW graph (web pages and hyperlinks)crawling
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Autonomous Systems (ASs)
AS: subnetwork under separate administrative control.
Examples:AS8: Rice UniversityAS378: ILANAS701: UUNETAS768: JANETAS20965: GEANT
An AS can consist of tens to thousands of routers andhosts.
roughly 15,000 ASs in 2003, 23,000 ASs in 2006.
Routing between ASs: BGP (border gateway protocol)
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AS786:
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AS20965: GEANT
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Traceroute: Leicester – Haifatraceroute: pc14.mcs.le.ac.uk → www.haifa.ac.il1 gate (143.210.72.1)2 143.210.6.2 (143.210.6.2)3 uol3-gw-7-1.emman.net (194.82.121.177)4 uol1-gw-g3.emman.net (212.219.212.85)5 uon6-gw-7-1.emman.net (194.82.121.25)6 nottingham-bar.ja.net (146.97.40.21)7 po12-0.lond-scr.ja.net (146.97.35.13)8 po6-0.lond-scr3.ja.net (146.97.33.30)9 po1-0.gn2-gw1.ja.net (146.97.35.98)10 janet.rt1.lon.uk.geant2.net (62.40.124.197)11 so-4-0-0.rt1.par.fr.geant2.net (62.40.112.105)12 so-7-3-0.rt1.gen.ch.geant2.net (62.40.112.29)13 so-2-0-0.rt1.mil.it.geant2.net (62.40.112.34)14 so-1-2-0.rt1.tik.il.geant2.net (62.40.112.121)15 iucc-gw.rt1.tik.il.geant2.net (62.40.124.126)16 haifa-gp0-cel-g.ilan.net.il (128.139.234.2)17 * * *
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Internet Mapping Projects
A map of the Internet can be obtained by combining thelocal views from a number of locations (vantage points):
Path data from traceroute experiments
Path data from BGP routing tables
Examples:
Bill Cheswick’s Internet Mapping Project (traceroute,router-level)
Oregon Route Views (based on BGP data, AS-level)
DIMES (Yuval Shavitt): router-level and AS-level, basedon volunteer community
and others
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Outline
AS Relationships and the Valley-Free Path Model
Inferring AS Relationships
Cuts and Disjoint Paths in the Valley-Free PathModel
Network Discovery and Verification
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AS Relationships and the
Valley-Free Path Model
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Undirected AS-Graph
An undirected AS-graph is a simple, undirected graph witha vertex for every ASan edge joining two vertices if the correspondingASs have at least one physical connection.
Example:
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AS Relationships
Customer-Provider: directed edge
CUSTOMER
PROVIDER
Customer pays provider for Internet access.
Peer-to-Peer: bidirected edge
PEER PEER
Peers exchange traffic of their subnetworks and theircustomers.
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AS Relationships
Customer-Provider: directed edge
CUSTOMER
PROVIDER
Customer pays provider for Internet access.
Peer-to-Peer: bidirected edge
PEER PEER
Peers exchange traffic of their subnetworks and theircustomers.
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AS-Graph
An AS-graph is a graph G = (V, E) in which any twovertices u, v ∈ V can
be non-adjacent,have a directed edge (u, v) or (v, u),or have a bidirected edge u, v.
Example:
Model by Subramanian et al., 2002.
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Routing Policies
Customers do not route traffic from one provider toanother:
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Routing Policies
Peers do not forward to other peers:
Peers do not forward from peers to providers (and viceversa):
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.14/55
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
VALID
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.14/55
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
VALID
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.14/55
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
VALID
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.14/55
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Valley-Free Paths
A path π from s to t in an AS-graph is valid in thevalley-free path model, if it consists of
a sequence of ≥ 0 forward edges,followed by 0 or 1 bidirected edges,followed by a sequence of ≥ 0 reverse edges.
Example:
INVALID
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Inferring AS Relationships
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Motivation
AS relationships are important for analyzing BGProuting, but difficult to obtain.
Idea: Use information about BGP paths to infer ASrelationships.
Initiated by [Gao, 2001].
Formalization as Type-of-Relationship (ToR) problem bySubramanian et al., 2002.
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ToR-Problem
Given:
undirected graph G, set P of paths in G.
Solution:
classification of edges of G into customer-provider and peer-to-peer relationships.
Objective:
maximize the number of paths in P that are made valid.
Special case: check if all paths in P can be valid.
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Example
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Example
All paths are valid!
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Example 2
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Example 2
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Example 2
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Example 2
Only one of the two paths can be valid!
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Results
There is a linear-time algorithm for deciding whether allpaths can be made valid ( 2SAT).
If not all paths can be made valid, the ToR-problem isNP-hard and APX-hard even if all paths have length 2.
In general, the ToR-problem cannot be approximatedwithin 1
n1−εfor n paths, unless NP = ZPP .
If the path lengths are bounded by a constant, theToR-problem can be approximated within a constantfactor (trivial algorithm: random orientation).
If the path length is at most 2, 3, or 4, we obtainapproximation ratio 0.94, 0.84, or 0.36 (using MAX2SAT[Goemans, Williamson 1994; Lewin, Livnat, Zwick2002]).
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Sketch of Algorithm
Don’t use peer-to-peer edges at all!
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Sketch of Algorithm
Initially, classify each edge arbitrarily.
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Sketch of Algorithm
Build a 2SAT formula representing a solution thatmakes all paths valid.
x1 x3x4
x5
x2
(x1 ∨ x2) ∧ (x2 ∨ x3) ∧ (x4 ∨ x3) ∧ (x5 ∨ x4)
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Sketch of Algorithm
Use MAX2SAT algorithm to obtaingood truth assignment for the variables.
x1 x3x4
x5
x2
(x1 ∨ x2) ∧ (x2 ∨ x3) ∧ (x4 ∨ x3) ∧ (x5 ∨ x4)
x1 = F, x2 = F, x3 = T, x4 = F, x5 = F
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Sketch of Algorithm
Flip directions of true variables.
x1 x4
x5
x2
x3
(x1 ∨ x2) ∧ (x2 ∨ x3) ∧ (x4 ∨ x3) ∧ (x5 ∨ x4)
x1 = F, x2 = F, x3 = T, x4 = F, x5 = F
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Comments on Relationship Inference
Maximizing the number of valid paths is not really theright objective function. We need to find a formulation ofthe ToR problem that yields more realisticclassifications:
Avoid customer-provider cycles.Include peer-to-peer edges.Include sibling edes.
Other direction: Use active probing methods to obtainbetter classifications.
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Cuts and Disjoint Paths
in the Valley-Free Path Model
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Robustness Considerations
Robustness of connectivity between s and t:Minimum size of a cut separating s and t.Maximum number of disjoint paths between s and t.
Efficiently computable using network flow techniques instandard undirected or directed graphs.
But: should take into account routing policies! valley-free path model
⇒ Problems Min Valid s-t-Cut and Max Disjoint Valids-t-Paths (vertex version and edge version).
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Min Valid s-t-Vertex-Cut
Given:
Directed graph G = (V, E) and two non-adjacentvertices s, t ∈ V
Feasible solution:
A valid s-t-vertex-cut C(C ⊆ V \ s, t s.t. @ valid s-t-path in G \ C)
Objective:
Minimize |C|.
Smallest number of ASs that must fail in order to disconnects and t with respect to valley-free paths.
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Max Vertex-Disjoint Valid s-t-Paths
Given:
Directed graph G = (V, E) and two non-adjacentvertices s, t ∈ V
Feasible solution:
Set P of vertex-disjoint valid s-t-paths in G
Objective:
Maximize |P|.
Largest number of disjoint valley-free paths connecting ASss and t.
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Example
s t
max number of vertex-disjoint s-t-paths:
min valid s-t-vertex-cut:
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Example
s t
max number of vertex-disjoint s-t-paths: 1
min valid s-t-vertex-cut:
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Example
ts
max number of vertex-disjoint s-t-paths: 1
min valid s-t-vertex-cut: 2
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Hardness Results
Theorem. Min Valid s-t-Vertex-Cut is APX-hard.
Proof. By reduction from 3-WAY EDGE CUT.
Theorem. Max Vertex-Disjoint Valid s-t-Paths is NP-hardand cannot be approximated with ratio 2 − ε for any ε > 0unless P = NP .
Proof. By reduction from 2DIRPATH.
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Main Result
Theorem. There is an efficient algorithm that computes avalid s-t-vertex-cut of size c and a set of d vertex-disjointvalid s-t-paths such that c ≤ 2 · d.
Corollary. There is a 2-approximation algorithm for MinValid s-t-Vertex-Cut and a 2-approximation algorithm forMax Vertex-Disjoint Valid s-t-Paths.
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Two-Layer Model
s tG
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Two-Layer Model
s t
G
reverse(G)H
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Paths in G and H
s tG s t
G
reverse(G)H
valid path in G ≡ directed path in H
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Cut-Algorithm
➀ Compute minimum s-t-vertex-cut CH in H.
➁ Output the set CG = v ∈ V (G) | ≥ 1 copy of v is in CHas valid s-t-cut.
Analysis:
|CG| ≤ |CH |, CG is valid s-t-vertex-cut
|CH | ≤ 2· size of min valid s-t-vertex-cut in G
2-approximation algorithm
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Path-Algorithm
➀ Compute max disjoint s-t-paths PH in H.
➁ Interpret PH as set PG of valid s-t-paths in G.
➂ Recombine parts of paths in PG to get at least 1
2|PG|
disjoint valid s-t-paths in G.
Observations:
Forward parts of paths in PG are disjoint.
Backward parts of paths in PG are disjoint.
Forward part of one path may intersect backward partsof other paths.
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Recombination
s t
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Summary of Results
For arbitrary directed graphs, valley-free path model:
Min s-t-Cut Max Disjoint s-t-Paths
vertex APX-hard no (2 − ε)-apx unless P = NP
version 2-approx 2-approxedge polynomial no (2 − ε)-apx unless P = NP
version 2-approx
(plus some additional results for DAGs)
Remark. Interesting cut and disjoint paths problems arisealso from paths with other restrictions (e.g. length-boundedpaths).
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Network Discovery
and Verification
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General Setting
Discover information about an unknown network usingqueries.
Verify information about a network using queries.
Here, “network” means connected, undirected graph.
Motivation: Internet mapping; discovering the linkstructure of peer-to-peer networks.
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Two Problems
Network Discovery:Task: Identify all edges and non-edges of the networkusing a small number of queries.
On-line problem (incomplete information), competitiveanalysis
Network Verification:Task: Check whether an existing network “map” iscorrect, using a small number of queries.
Off-line problem (full information), approximationalgorithms
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Query Models
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Layered-Graph (LG) Query Model
Connected graph G = (V, E) with |V | = n (in the on-linecase, only V is known in advance)
Query at node v ∈ V yields the subgraph containing allshortest paths from v to all other nodes of G.
Problem LG-ALL-DISCOVERY (LG-ALL-VERIFICATON):Minimize the number of queries required to discover(verify) all edges and non-edges of G.
Observation. Query at v discovers all edges andnon-edges between vertices with different distance from v.
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Layered-Graph (LG) Query Model
Connected graph G = (V, E) with |V | = n (in the on-linecase, only V is known in advance)
Query at node v ∈ V yields the subgraph containing allshortest paths from v to all other nodes of G.
Problem LG-ALL-DISCOVERY (LG-ALL-VERIFICATON):Minimize the number of queries required to discover(verify) all edges and non-edges of G.
Observation. Query at v discovers all edges andnon-edges between vertices with different distance from v.
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Layered-Graph Query Example
Three queries are sufficient!
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Layered-Graph Query Example
Three queries are sufficient!
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Layered-Graph Query Example
Three queries are sufficient!
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Layered-Graph Query Example
1
1 2
2
3
4
4
Three queries are sufficient!
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Layered-Graph Query Example
Three queries are sufficient!
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Layered-Graph Query Example
Three queries are sufficient!
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Layered-Graph Query Example
2
2 22
21
1
Three queries are sufficient!
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Layered-Graph Query Example
Three queries are sufficient!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.41/55
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Layered-Graph Query Example
Three queries are sufficient!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.41/55
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Layered-Graph Query Example
Three queries are sufficient!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.41/55
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Layered-Graph Query Example
Three queries are sufficient!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.41/55
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Distance (D) Query Model
Connected graph G = (V, E) with |V | = n (in the on-linecase, only V is known in advance)
Query at node v ∈ V yields the distances between vand all other nodes of G.
Problem D-ALL-DISCOVERY (D-ALL-VERIFICATON):Minimize the number of queries required to discover(verify) all edges and non-edges of G.
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Distance Query Example
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Distance Query Example
Query 1:
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Distance Query Example
Query 1:
1
21
0
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Distance Query Example
Query 1:
?
?
?
1
21
0
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Distance Query Example
Query 1:
?
?
?
Query 2:
1
21
0
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Distance Query Example
Query 1:
?
?
?
Query 2:
0
1
1
2
1
21
0
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Distance Query Example
Query 1:
?
?
?
Query 2:
??
?
0
1
1
2
1
21
0
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Distance Query Example
Query 1:
?
?
?
Query 2:
??
?
0
1
1
2
1
21
0
Blue edge is discovered by combination of queries!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.43/55
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Distance Query Example
Query 1:
?
?
?
Query 2:
??
?
0
1
1
2
1
21
0
Two queries are sufficient!
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Results for LG Query Model
LG-ALL-DISCOVERY:No deterministic algorithm can be better than3-competitive.There is a randomized algorithm that isO(
√n log n)-competitive.
LG-ALL-VERIFICATION:Optimal number of queries is equivalent to metricdimension of the graph.NP-hard to approximate within o(log n)
O(log n)-approximation using greedy set coveralgorithm [Khuller et al., 1996]
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Distance Query ModelA query at v discovers the distances to all other nodes.
For the LG model, the edges and non-edges discoveredby a set of queries were simply the union of thosediscovered by the individual queries. This is not true foredges in the distance query model!
Query 1:
?
?
?
Query 2:
??
?
0
1
1
2
1
21
0
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Discovering Non-edges in the D Model
Lemma. A set Q of queries discovers a non-edge u, v ifand only if there is q ∈ Q with |d(q, u) − d(q, v)| ≥ 2.
i >i+1
uv
q
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Discovering Edges in the D Model
Definition. A query q is a partial witness for edge u, v ifd(q, u) 6= d(q, v) (say, d(q, u) = i and d(q, v) = i + 1) and u isthe only neighbor of v at distance i from q.
i
i+1i+2
u vq
Lemma. A set Q of queries discovers all edges andnon-edges of G if and only if it discovers all non-edges andcontains a partial witness for each edge.
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Competitive Lower Bound
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.48/55
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Competitive Lower Bound
Optimal number of queries: 2
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Competitive Lower Bound
Deterministic algorithm: First query in rightmost branch.
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Competitive Lower Bound
A smaller tree of the same kind remains.
Nodes in each level indistinguishable to the algorithm.
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Competitive Lower Bound
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Competitive Lower Bound
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Competitive Lower Bound
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.48/55
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Competitive Lower Bound
Theorem. No deterministic algorithm can havecompetitive ratio better than Θ(
√
n) for D-ALL-DISCOVERYin graphs with n nodes.
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Competitive Lower Bound
Theorem. No randomized algorithm can have competitiveratio better than Θ(log n) for D-ALL-DISCOVERY in graphswith n nodes.
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.49/55
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.49/55
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.49/55
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.49/55
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Ideas for an On-Line Algorithm
View problem as a hitting set problem: For edge e, hitthe set of its partial witnesses, and for non-edge e, hitthe set of queries that discover it.
Every non-edge can either be discovered by many(more than T ) queries or by few (at most T ) queries.
Similarly, every edge has either many partial witnessesor few.
Use random queries to discover all non-edges that canbe discovered by many queries, and to get a partialwitness for every edge that has many partial witnesses.
For each remaining undiscovered non-edge [edge],query all vertices that discover it [all partial witnesses].
With T =√
n ln n we get competitive ratio O(√
n log n ).
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.49/55
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Algorithm
Phase 1: Choose 3√
n ln n vertices uniformly at randomand query them.
Phase 2: While there is an undiscovered (non-)edgebetween some vertices u and v, do:
query u and v
if u, v is non-edge, query all vertices that discoveru, v.
if u, v is an edge and d(u), d(v) ≤√
n/ ln n, queryall neighbors of u and v and then all vertices that arepartial witnesses for u, v.otherwise, proceed with another undiscovered(non)-edge
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Algorithm for D-ALL-DISCOVERY
Theorem. There is a randomized on-line algorithm forD-ALL-DISCOVERY that achieves competitive ratioO(
√n log n).
Proof Ideas:
With probability at least 1 − 1
n , Phase 1 discovers allnon-edges that are discovered by many (i.e., more thanT =
√n ln n) queries and contains partial witnesses for
all edges that have many partial witnesses.
In Phase 2, if the case that u or v has more than√
n/ ln n neighbors happens k times, OPT is at least
k
√n/ ln n2n = k
2√
n ln n, so these iterations do not hurt the
competitive ratio.
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Results for D-ALL-VERIFICATION
D-ALL-VERIFICATION is NP-hard.Proof by reduction from vertex cover problem.
There is an O(log n)-approximation algorithm forD-ALL-VERIFICATION.
Simply apply the greedy set cover approximationalgorithm.
The cycle Cn, n > 6, can be verified optimally with 2queries.
The hypercube Hd, d ≥ 3, can be verified optimally with2d−1 queries.
There is a polynomial algorithm forD-ALL-VERIFICATION in trees.
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Summary of Network Discovery Results
LG model:Discovery: randomized upper bound O(
√n log n),
deterministic lower bound 3.Verification: Θ(log n)-approximable.
D model:Discovery: randomized upper bound O(
√n log n),
deterministic lower bound Ω(√
n), randomized lowerbound Ω(log n).Verification: NP-hard, O(log n)-approximation.
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.53/55
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Open Problems
Close the gaps between upper and lower bounds forcompetitive ratio of network discovery problems.
Deterministic on-line algorithms for network discovery?
Better approximation for D-ALL-VERIFICATION?
Better results for special graph classes?
Models where queries can be made only at a subset ofthe nodes of the graph (motivated by practicalapplications).
Approximate discovery/verification: e.g., discover 95%of edges and 95% of non-edges.
Discovering graph properties.
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.54/55
![Page 122: Algorithmic Problems Related to Internet Graphs · ToR-Problem Given: undirected graph G, set P of paths in G. Solution: classification of edges of G into customer- provider and](https://reader034.fdocuments.us/reader034/viewer/2022042402/5f13cdb3f033a521237e6a13/html5/thumbnails/122.jpg)
Thank you!
T. Erlebach – Algorithmic Problems Related to Internet Graphs – Sixth Haifa Workshop on Interdisciplinary Applications of Graph Theory, Combinatorics, and Algorithms – May ’06 – p.55/55