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Capacity of Agreement with Finite Link CapacityPowerPoint Presentation

Capacity of Agreement with Finite Link Capacity

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### Capacity of Agreementwith Finite Link Capacity

Guanfeng Liang @ Infocom 2011

Electrical and Computer Engineering

University of Illinois at Urbana-Champaign

Joint work with Prof. NitinVaidya

Motivation

- Distributed systems are emerging
- Cloud computing (e.g. Windows Azure), distributed file systems, data centers, multiplayer online games

- Large number of distributed components
- Distributed components need to be coordinated

Motivation

- Distributed primitives
- Clock synchronization
- Mutual exclusion
- Agreement
- etc.

- Large body of literature in Distributed Algorithms

Motivation

A networking guy asks:

“How would constraints of the network affect the performance of these primitives?”

A algorithm guy replies: “……”

Network-aware

distributed algorithm design

Byzantine agreement in p2p networks

Byzantine Agreement (BA): Broadcast

- A sender wants to send message to n-1 receivers
- Fault-free receivers must agree
- Sender fault-free agree on its message
- Any ≤ f nodes may fail

Why agreement?

- Distributed systems are failure-prone
- Non-malicious: crashed nodes, buggy codes
- Malicious: attacker tries to crack the system

- Robust system against faults:

Important to maintain consistent state

Impact of the Network

- How does capacity (rate region) of the network affect agreement performance?
- How to quantify the impact?

Rate Region

- Defines the way “links” may share channel
- Interference posed to each other determines whether a set of transmissions can succeed together

Point-to-Point Network Rate Region

Rate ij≤ Capacity ij

S

Each directed linkindependent of other links

1

2

Capacity of Agreement

- b(t) = # bits agreed in [0,t]
- Capacity of agreement: supremum of achievable throughput for a given rate region

Upper Bound of Capacity in P2P Networks

- NC1: C ≤ min-cut(S,X | freceivers removed)
- NC2: C ≤ In(X | f nodes removed)

S

ε

3

1

2

Upper bound = 1+ε

Classic Solution for Broadcast

value v

S

v

v

v

3

1

v

v

v

2

Majority

vote resultsin correctresult atgood receiver

v

?

v

?

v

Classic Solution for Broadcast

S

v

x

w

3

1

[v,w,x]

w

w

[v,w,x]

2

x

v

[v,w,x]

Vote resultidentical atgood receivers

v

x

Classic Solution in P2P Networks

- Whole message is sent on every link

S

Throughput ≤ slowest link

ε

3

1

Throughput≤ ε

but

Upper bound = 1+ε

2

Improving Broadcast Throughput

- Observation: classic solution is in fact an “error correction code”
- “Error detection codes” are more efficient

Error Detection Code

Two-bit value

a, b

S

a

a+b

b

3

1

b

[a,b,a+b]

b

[a,b,a+b]

2

a+b

a

[a,b,a+b]

a

a+b

Error Detection Code

Two-bit value

a, b

S

a

a+b

b

3

1

b

[a,b,a+b]

b

[a,b,a+b]

2

a+b

a

[a,b,a+b]

Parity check passes

at all nodes

Agree on (a,b)

a

a+b

Error Detection Code

Two-bit value

a, b

S

a

a+b

b

3

1

b

[?,b,a+b]

b

2

a+b

?

[?,b,a+b]

Parity checkfails at a node

if 1 misbehaves

?

a+b

Error Detection Code

Two-bit value

a, b

Only detection is

not what we want

S

a

z

b

3

1

b

[a,b,z]

b

[a,b,z]

2

z

a

[a,b,z]

Check fails

at a good node

if S sends bad codeword (a,b,z)

a

z

Modification

- Agree on small pieces of data in each “round”
- If X misbehaves with Y in a given round, avoid using XY link in the next round (for next piece of data)
- Repeat

Algorithm Structure

- Fast round (as in the example)

Algorithm Structure

- Fast round (as in the example)

S

a

a+b

b

3

1

b

[a,b,a+b]

b

[a,b,a+b]

2

a+b

a

[a,b,a+b]

a

a+b

Algorithm Structure

- Fast round (as in the example)
- Fast round
…

- Fast round in which failure is detected
- Expensive round to learn new info about failure

Algorithm Structure

- Fast round (as in the example)
- Fast round
…

- Fast round in which failure is detected
- Expensive round to learn new info about failure
- Fast round
- Fast round
…

- Expensive round to learn new info about failure.

Algorithm Structure

- Fast round (as in the example)
- Fast round
…

- Fast round in which failure is detected
- Expensive round to learn new info about failure
- Fast round
- Fast round
…

- Expensive round to learn new info about failure.

After a small number of expensive rounds, failures completely identified

Algorithm Structure

- Fast round (as in the example)
- Fast round
…

- Fast round in which failure is detected
- Expensive round to learn new info about failure
- Fast round
- Fast round
…

- Expensive round to learn new info about failure.
- Only fast rounds hereon

After a small number of rounds failures identified

Algorithm “Analysis”

- Many fast rounds
- Few expensive rounds
- When averaged over time,the cost of expensive rounds is negligible
- Average usage of link capacity depends only on the fast round, which is very efficient

Achieves capacity for 4-node networks, and symmetric networks

Open Problems

- Capacity of agreement for general rate regions

Open Problems

- Capacity of agreement for general rate regions
- Even the multicast problem with Byzantine nodes is unsolved - For multicast, sources fault-free

Rich Problem Space

- Wireless channel allows overhearing
- Transmit to 2 at highrate, or low rate ? - Low rate allows reception at 1

1

2

S

3

Rich Problem Space

- Similar questions relevant for anymulti-party computation

Distributed

Computation

Communication

Multi-party computing under

Communication Constraints

How many bits needed?

- N nodes each has a k-bit input
- Check if all inputs are identical
- At least 1 node “detects” if not identical

2

Intuitive guess: (N-1)k bit

Is it the best we can do?

1

3

Improving Broadcast Throughput

- Observation: classic solution is in fact an “error correction”
- “Error detection” suffices
- Disseminate some data
- Check if consistent or not
- Consistent: decide
- Inconsistent: diagnose and adapt

- Repeat for new data

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