On the Capacity of Information Networks Nick Harvey Collaborators: Micah Adler (UMass), Kamal Jain...

On the Capacity of Information Networks

Nick Harvey

Collaborators: Micah Adler (UMass), Kamal Jain (Microsoft), Bobby Kleinberg (MIT/Berkeley/Cornell), and April Lehman (MIT/Google)

What is the capacity of a network?

s1 s2

Send items from s1t1 and s2t2

Problem: no disjoint paths

bottleneck edge

What is the capacity of a network?

t2 t1

b1⊕b2

An Information Networkb1 b2s1 s2

t2 t1

If sending information, we can do better Send xor b1⊕b2 on bottleneck edge

Moral of Butterfly

Network Flow Capacity≠

Information Flow Capacity

Network Coding New approach for information flow

problemsBlend of combinatorial optimization,

information theoryMulticast, k-Pairs

k-Pairs problems: Network coding when each commodity has one sinkAnalogous to multicommodity flow

Definitions for cyclic networks are subtle

MulticommodityFlow

Efficient algorithms for computing maximum concurrent (fractional) flow.

Connected with metric embeddings via LP duality.

Approximate max-flow min-cut theorems.

NetworkCoding

Computing the max concurrent coding rate may be: Undecidable Decidable in poly-time

No adequate duality theory.

No cut-based parameter is known to give sublinear approximation in digraphs.

Directed and undirected problems behave quite differently

Coding rate can be muchlarger than flow rate!

Butterfly: Coding rate = 1 Flow rate = ½

Thm [HKL’04,LL’04]: graphs G(V,E) whereCoding Rate = Ω( flow rate ∙ |V| )

Directed k-pairss1 s2

t2 t1

Thm: graphs G(V,E) whereCoding Rate = Ω( flow rate ∙ |E| ) And this is optimal Recurse on butterfly construction

Coding rate can be muchlarger than flow rate!

…and much larger than the sparsity(same example)

Directed k-pairs

Flow Rate Sparsity < Coding Rate

in some graphs

No known undirected instance where coding rate ≠ max flow rate!

(The undirected k-pairs conjecture)

Undirected k-pairs

Flow Rate Coding Rate Sparsity

Pigeonhole principle argumentGap can be Ω(log n) when G is an expander

Undirected k-Pairs Conjecture

Flow Rate Sparsity Coding Rate

< =? ?= <

Undirected k-pairs conjecture

Unknown until this work

Okamura-Seymour Graphs1

t1s2

t2s3

t3

s4 t4

Every cut has enough capacity to carry all commodities separated by the cut

Cut

Okamura-Seymour Max-Flows1

t1s2

t2s3

t3

s4 t4

Flow Rate = 3/4

si is 2 hops from ti.

At flow rate r, each commodity consumes 2r units of bandwidth in a graph with only 6 units of capacity.

The trouble with information flow… If an edge codes

multiple commodities, how to charge for “consuming bandwidth”?

We work around this obstacle and bound coding rate by 3/4.

s1

t1s2

t2s3

t3

s4 t4

At flow rate r, each commodity consumes at least 2r units of bandwidth in a graph with only 6 units of capacity.

Definition: A e if for every coding solution,the messages sent on edges of A uniquely determine the message sent on e.

Given A and e, how hard is it to determine whether A e? Is it even decidable?

Theorem: There is an algorithm tocompute whether A e in time O(k²m). Based on a combinatorial characterization

of informational dominance

Informational Dominance

i

i

i

What can we prove?

s1 t3

s2 t1

s3 t2

s4 t4

Combine Informational Dominance with Shannon inequalities for Entropy

Flow rate = coding rate for “Special Bipartite Graphs”: Bipartite Every source is 2 hops

away from its sink Dual of flow LP is optimized

by assigning length 1 to all edges

Next: show that proving conjecture for all graphs is quite hard

k-pairs conjecture & I/O complexity

I/O complexity model [AV’88]:A large, slow external memory consisting of

pages each containing p recordsA fast internal memory that holds 2 pagesBasic I/O operation: read in two pages from

external memory, write out one page

I/O Complexity of Matrix Transposition

Matrix transposition: Given a p×p matrix of records in row-major order, write it out in column-major order.

Obvious algorithm requires O(p²) ops.

A better algorithm uses O(p log p) ops.


Matrix transposition: Given a p×p matrix of records in row-major order, write it out in column-major order.



s1 s2


Matrix transposition: Given a pxp matrix of records in row-major order, write it out in column-major order.



s1 s2 s3 s4





s1 s2 s3 s4

t1t3





s1 s2 s3 s4

t1t3t2

t4

Matching Lower Bound Theorem: (Floyd ’72,

AV’88) A matrix transposition algorithm using only read and write operations (no arithmetic on values) must perform Ω(p log p) I/O operations.

s1 s2 s3 s4

t1t3t2

t4

Ω(p log p) Lower Bound

Proof: Let Nij denote the number of ops in which record (i,j) is written. For all j,

Σi Nij ≥ p log p.

Hence

Σij Nij ≥ p² log p.

Each I/O writes only p records. QED.

s1 s2 s3 s4

t1t3t2

t4

The k-pairs conjecture and I/O complexity

Definition: An oblivious algorithm is one whose pattern of read/write operations does not depend on the input.

Theorem: If there is an oblivious algorithm for matrix transposition using o(p log p) I/O ops, the undirected k-pairs conjecture is false.

s1 s2 s3 s4

t1t3t2

t4


Proof: Represent the algorithm

with a diagram as before.

Assume WLOG that each node has only two outgoing edges. s1 s2 s3 s4

t1t3t2

t4


Proof: Represent the algorithm

with a diagram as before.

Assume WLOG that each node has only two outgoing edges.

Make all edges undirected, capacity p.

Create a commodity for each matrix entry.

s1 s2 s3 s4

t1t3t2

t4


Proof: The algorithm itself is a

network code of rate 1. Assuming the k-pairs

conjecture, there is a flow of rate 1.

Σi,jd(si,tj) ≤ p |E(G)|.

Arguing as before, LHS is Ω(p² log p).

Hence |E(G)|=Ω(p log p).

s1 s2 s3 s4

t1t3t2

t4

Other consequences for complexity

The undirected k-pairs conjecture implies:A Ω(p log p) lower bound for matrix

transposition in the cell-probe model.

[Same proof.]A Ω(p² log p) lower bound for the running time

of oblivious matrix transposition algorithms on a multi-tape Turing machine.

[I/O model can emulate multi-tape Turing machines with a factor p speedup.]

Distance arguments Rate-1 flow solution implies Σi d(si,ti) ≤ |E|

LP duality; directed or undirected Does rate-1 coding solution imply

Σi d(si,ti) ≤ |E|?Undirected graphs: this is essentially the

k-pairs conjecture!Directed graphs: this is completely false

k commodities (si,ti)

Distance d(si,ti) = O(log k) i O(k) edges!

Recursive construction

s(1) s(2) s(3) s(4) s(5) s(6) s(7) s(8)

t(1) t(2) t(3) t(4) t(5) t(6) t(7) t(8)

Recursive Constructions1 s2

t1 t2

G (1):

Equivalent to:

s1s2

t1 t2

Edge capacity= 1

2 commodities7 edgesDistance = 3

Recursive Constructions1 s2 s3 s4

t1 t2 t3 t4

G (2):

Start with two copies of G (1)


t1 t2 t3 t4

G (2):

Replace middle edges with copy of G (1)


G (1)

t1 t2 t3 t4

G (2):

4 commodities, 19 edges, Distance = 5

Recursive Construction

G (n-1)G (n):

# commodities = 2n, |V| = O(2n), |E| = O(2n) Distance = 2n+1

s1 s2

t1 t2

s3 s4

t3 t4

s2n-1 s2n

t2n-1 t2n

Summary Directed instances:

Coding rate >> flow rate Undirected instances:

Conjecture: Flow rate = Coding rateProof for special bip graphsTool: Informational DominanceProving conjecture solves Matrix

Transposition Problem

Open Problems Computing the network coding rate in DAGs:

Recursively decidable? How do you compute a o(n)-factor approximation?

Undirected k-pairs conjecture:Stronger complexity consequences?Prove a Ω(log n) gap between sparsest cut

and coding rate for some graphs…or, find a fast matrix transposition algorithm.

Backup Slides

Optimality

The graph G (n) proves:Thm [HKL’05]: graphs G(V,E) whereNCR = Ω( flow rate ∙ |E| )

G (n) is optimal:Thm [HKL’05]: graph G(V,E),NCR/flow rate = O(min {|V|,|E|,k})

s1 s2

t2 t1

A does not dominate B


Def: A dominates B if information in A determines information in Bin every network coding solution.


Def: A dominates B if information in A determines information in Bin every network coding solution.

s1 s2

t2 t1

A dominates B

Sufficient Condition: If no path fromany source B then A dominates B

Informational Dominance Examples1 s2

t1

t2

“Obviously” flow rate = NCR = 1 How to prove it? Markovicity? No two edges disconnect t1 and t2 from both sources!


t1

t2

Cut A

Sufficient Condition: If no path from any source B then A dominates B


t1

t2

Our characterization implies thatA dominates {t1,t2} H(A) H(t1,t2)

Cut A

Rate ¾ for Okamura-Seymour

s1 t3

s2 t1

s3 t2

s4 t4

s1

i

s1 t3

s3


s1 t3

s2 t1

s3 t2

s4 t4

i

i

i + +

≥ + +


s1 t3

s2 t1

s3 t2

s4 t4

i

i

i + +

≥ + +i


s1 t3

s2 t1

s3 t2

s4 t4

+ + ≥ +

i i

≥


s1 t3

s2 t1

s3 t2

s4 t4

+ + ≥ +

≥

3 H(source) + 6 H(undirected edge) ≥ 11 H(source)6 H(undirected edge) ≥ 8 H(source)

¾ ≥ RATE

On the Capacity of Information Networks Nick Harvey Collaborators: Micah Adler (UMass), Kamal Jain...

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Transcript of On the Capacity of Information Networks Nick Harvey Collaborators: Micah Adler (UMass), Kamal Jain...