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A Reduc(on Approach to the Mul(ple-‐Unicast Conjecture in Network Coding
Zongpeng Li
What is Network Coding?
• Encoding data during a mul(-‐hop transmission – mul(ple unicasts – mul(cast
Coding Advantage
• Improve throughput for mul(cast
Coding Advantage
• Improve throughput for mul(ple-‐unicast
t2 t1
s1 s2 a b
a+b a b
Coding Advantage
• Save bandwidth – Network Coding: 9 bits – Rou(ng: 10 bits
t2 t1
s1 s2 a b
a+b a b
Network Models
Directed Networks • Not necessarily bidirec(onal • A pair of reverse links each
has its own capacity
Undirected Networks • Bidirec(onal • Capacity can be freely
allocated to two direc(ons
2
3 6 4
4
5
6 4
4
Coding Advantage in Undirected Networks
• Improve throughput for mul(cast – Up to a bounded factor
• Network Coding: 2 bps • Rou(ng: 1.875 bps
LeVer: 0.25bps; Number: 0.125bps
Coding Advantage in Undirected Networks
• Reduce cost for mul(cast
Rou(ng: 4.64 Network Coding: 4.57
Mul(ple-‐Unicast + Undirected Networks?
Coding advantage vanishes!
a
a
b
ba+b
a+ba+b
s1
t2
a1
b1
a1
b1
b2a1
a2
b2
a2
b2
a2 b1
s1
t2
2
t1
s2
t1
s
Another Example
a
a b b c
ca+b
a+b
a+b
b+c
b+ca+ca+b+c
a
a
b c
c b
b+c
a b c
ac b
a2
c1
a1
c1
b1c1
b2
c1
c1
b2
c2
b2
a1 b1
a1
c2
a1b2
c2b1
c2b1
c2
a2 a2b1 a2
b2
a1a2
The Conjecture
In terms of improving throughput or saving bandwidth, Network coding has no advantage over rou(ng for mul(ple unicast sessions in undirected networks. [Li and Li 2004]
Comments
• Mitzenmacher : No.1 of seven open problems in network coding (2007)
• Chekuri : “bold conjecture”, the problem of fully understanding network coding for mul(ple unicast sessions is s(ll “wild open”.
• Adler : “arguably the most important open problem in the field of network coding” (2006)
• The conjecture implies an affirma(ve answer to a 28-‐year-‐old open problem.
Verified Cases
• 2 unicast sessions • Terminal co-‐face planar networks • Complete networks with uniform link length • Grid networks with uniform link length and aligned source-‐receivers
• Each source is closer to its receiver than other receivers
Verified Cases
• Okamura-‐Seymour Network (K3,2)
• Hu’s 3-‐commodity network
• Complete bipar(te networks with uniform link length
s1 t1
t3
s3
s2
t2
t4
s4
s1 t1 t3 s3
s2
t2
Overview of our reduc(on approach
Undirected)Networks�
)�
Atom)Networks�
…�…�
Decompose�
Cut6set)Bound:)No� Theorem)1:)No�
) �
Require)Coding?�
Assemble� Theorem)3:)No)need)to)code)in)networks)that)can)be)decomposed)into)these)atoms)networks.�
Theorem)2:))when)&)how)to)decompose�
?�
Highlights of our results
• Generalize proofs of verified cases • Prove the conjecture for up to 6 nodes & most 7-‐node networks
• Find an interes(ng example where new techniques may be necessary
Cost Domain
• Link capacity is ignored • Each link is assigned with a non-‐nega(ve length le
• Let fe denote the amount of informa(on transmiVed on link e
• Cost: Σe fe le
Rela(ons Between Cost Domain and Throughput Domain
The conjecture in Cost Domain
Basic Techniques -‐-‐ inequali(es
• Cut-‐set: a set of edges dividing nodes into two parts
• Cut-‐set bound: F fe
e∈F∑ ≥ H (Xi )
i∈Sep(F )∑
Example for the cut-‐set bound
• Unit link length • For each cut-‐set Fj:
• Sum up:
t2 t1
s1 s2
fee∈Fj
∑ ≥ H (X1)+H (X2 )
F1
F2 F3
fee∈E∑ ≥ 3H (X1)+3H (X2 )
Overview of our reduc(on approach
Undirected)Networks�
)�
Atom)Networks�
…�…�
Decompose�
Cut6set)Bound:)No� Theorem)1:)No�
) �
Require)Coding?�
Assemble� Theorem)3:)No)need)to)code)in)networks)that)can)be)decomposed)into)these)atoms)networks.�
Theorem)2:))when)&)how)to)decompose�
?�
An observa(on
• Under the condi(on Network coding is necessary in G1 iff it is necessary in G2
t2 t1
s1 s2
t2 t1
s1 s2
F2
fee∈F2
∑ ≥ H (X1)+H (X2 )
Contract edges in F2
G1 G2
Generalize the idea
• Cut-‐set à Arbitrary edge set F • The problem is about the condi(on:
fee∈F2
∑ ≥ H (X1)+H (X2 )
fee∈F∑ ≥ ?
i∑ H (Xi )
An Equivalent form of the conjecture
Explana(on
• An edge set F decomposes G in to G/F and G/F.
t2 t1
s1 s2
t2 t1
s1 s2
F2
Decompose
t2 t1
s1 s2
• As long as the decomposi(on preserves the distance between each pair of source-‐receiver:
• Network coding is unnecessary in G/F and G/F è it is unnecessary in G. – Cost of Network Coding: – Cost of Rou(ng:
fee∈E∑ = fe
e∈F∑ + fe
e∈F∑
dG (si, ti )H (Xi )i∑ = dG/F (si, ti )H (Xi )
i∑ + dG/F (si, ti )H (Xi )
i∑
When there exists a decomposi(on
• An example – dG(s,t) = 2 – dG/F(s,t) =dG/F(s,t) = 0
• A path p in G à length |p F| in G/F length |p F| in G/F • There exist two shortest paths p1,p2 in G:
|p1 F|≠|p2 F|
s
t
F
∩
∩
∩ ∩
When there exists a decomposi(on
• Another example • Observa(on: – Non-‐shortest paths have some redundancy
– Shortest paths intersect F the minimum (me
s
t F
1 > 0 + 0
2 = 2 + 0
scale up link length
When there exists a decomposi(on
Theorem 2 If there is an edge set F that is compa(ble with all sessions, there exists a decomposi(on.
Overview of our reduc(on approach
Undirected)Networks�
)�
Atom)Networks�
…�…�
Decompose�
Cut6set)Bound:)No� Theorem)1:)No�
) �
Require)Coding?�
Assemble� Theorem)3:)No)need)to)code)in)networks)that)can)be)decomposed)into)these)atoms)networks.�
Theorem)2:))when)&)how)to)decompose�
?�
When the cut-‐set bound is insufficient
• Intui(vely, we need to combine several fe to show that their sum is no less than some H(Xi).
• Consider the following solu(on:
• LHS:
a d s1
b Xab
c
Xba
Xca
Xac
t1
s2
t2
fab + fac ≥ H (X1)
fab = H (Xab )+H (Xba )
fac = H (Xac )+H (Xca )
Xab = Xbd = X1 Xba = Xac = X2
fab + fac = H (X1)+ 2H (X2 )Loss!
A Finer Technique -‐-‐ Informa(on Inequality
• Use instead of the combined version
• Submodularity
– Here A,B are sets of variables Xi , Xuv
fuv
H (Xuv ),H (Xvu )
H (A)+H (B) ≥ H (A∪B)+H (A∩B)
Flexible!
Might save some loss!
• If messages B are determined by messages A – H(A) ≥ H(B)
• Input-‐output Inequality – The messages leaving node set U are determined by the messages entering U
• Crypto Inequality – A source message is determined by the messages transmiVed through a cut-‐set separa(ng the source and the receiver.
Example using informa(on inequali(es
s1 t1
t3
s3
s2
t2
t4
s4
d a b
c
e
H (Xac )+H (Xbc )+H (X2 )≥ H (Xac,Xbc,X2 )≥ H (Xac,Xbc,X2,X4,Xca,Xcb )
brought in by the Input-‐output Inequality
Similarly, combine messages enters d and e, respec(vely. We obtain
H (Xad,Xbd,X3,X2,Xda,Xdb )H (Xae,Xbe,X4,X3,Xea,Xeb )
Borrowed
Example using informa(on inequali(es
s1 t1
t3
s3
s2
t2
t4
s4
d a b
c
e
H (Xac,Xbc,X2,X4,Xca,Xcb )+H (Xad,Xbd,X3,X2,Xda,Xdb )≥ H (...,X2,X3,X4 )+H (X2 )
Then combine the 3 resul(ng entropies:
H (...,X2,X3,X4 )+H (Xae,Xbe,X4,X3,Xea,Xeb )≥ H (XE,X2,X3,X4 )+H (X3,X4 )
returned
set of messages on every link
Example using informa(on inequali(es
s1 t1
t3
s3
s2
t2
t4
s4
d a b
c
e
To sum up,
H (XE,X2,X3,X4 )≥ H (XE,X2,X3,X4,X1)≥ H (X1)+H (X2 )+H (X3)+H (X4 )
crypto inequality
source independent
H (Xuv )u=a,b; v=c,d,e∑ ≥ H (Xi )
i=1,2,3,4∑
Similarly, we can derive H (Xvu )
u=a,b; v=c,d,e∑ ≥ H (Xi )
i=1,2,3,4∑
Lessons Learned from the example
• Splixng fe into H(Xuv) and H(Xvu) is helpful. • Entropy terms H(A) can be combined in a cascade way. – we first combine the entropies of messages entering each node, then combine the resul(ng entropies.
• Borrowing source messages to trigger the input-‐output inequality is OK. – what actually maVers is the number of source messages brought into the deriva(on by the input-‐output/crypto inequality.
• We study the edge sets that are a liVle bit more complicate than cut-‐sets – the union of two cut-‐sets
• For such an edge set F, we find a way to combine the entropy terms to derive that
where zi equals dG/F(si,ti) for one session and min{2, dG/F(si,ti)} for the other sessions.
H (Xuv )+H (Xvu )e=uv∈F∑ ≥ ziH (Xi )
i∑
s1 t1
t3
s3
s2
t2
t4
s4 F1 F2
s1 t1 t3 s3
s2
t2 F1 F2
Proof of Theorem 1
• Each component is labeled according to its distance to s in G/F.
s t
F1
F2
Connected Component
U0
U1
U’1
U3
U2 U4
Proof of Theorem 1 (cont.)
• Step 1: combine the entropies of messages entering each component Ui;
• Step 2: combine the resul(ng entropies of U1 and U’1
• Step 3: similarly, combine U1 ,U’1,U3; combine U0, U2, U4.
Combine results together
A cut-‐set F is orthogonal to session i, if each shortest si-‐ti path crosses F at most once.
Remarks
• Condi(ons P1 and P2 only relate to cut-‐sets and shortest paths.
• Can be verified in (me O(2^|V|), in contrast to O(2^|E|) for the state-‐of-‐art LP outer-‐bound.
The Next Atom Network
Examine the Next Atom Network
s1
t1
t3
s3
s2
t2
s1
t1
t3
s3
s2
t2
Conclusion
• A Reduc(on Approach – brings the abstract conjecture to concrete small networks
• Prove the conjecture for up to 6 nodes • An interes(ng example for future research
Q&A
• Thanks for your (me!
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