Antfarm: Efficient Content Distribution with Managed Swarms

Ryan S. Peterson, Emin Gun SirerUSENIX NSDI 2009

Presented by:John Otto, Hongyu Gao

2009. 10. 21.Adapted from the slides of Eunsang Cho

Contents

• Problem Definition• Antfarm– Peer’s Perspective

– Coordinator’s Perspective

• Evaluation• Conclusion

2

Problem Definition

• To find an efficient way to disseminate a large set of files to a potentially very large set of clients

3

Existing Approaches

• Client-server– Pros: simple due to central authority

– Cons: cost and scalability

4

Existing Approaches

• Peer-to-peer swarms– Pros: reduced cost

– Cons: limited information, no control or performance guarantees

5

Goals

• High performance• Low cost of deployment• Performance guarantees– Administrator can control over swarm

performance

• Scalability

6

Antfarm

• Hybrid peer-to-peer architecture

• Content distribution optimization problem– Central authority (coordinator) makes decision

how to allocate bandwidth optimally.

7

System Overview

• Seeder: trusted servers managed by the coordinator that distribute data blocks to peers.

seeder

coordinator

swarm

8

Peer’s Perspective

• Default behavior– For peer and block selection is identical to

BitTorrent

• Advisory notification– Coordinator sends lists of underutilized peers as

candidates for data exchange.

• Token exchange– Incentive to data upload

9

Coordinator’s Perspective

• Coordinator– Collects statistics on peer network behavior

– Computes response curves and bandwidth allocations

– Steers the swarm toward an efficient operating point using token supply

• Formulation–Maximize system-wide aggregate bandwidth

subject to a bandwidth constraint

10

Constrained Optimization Problem

• Response curve– Critical properties of each swarm

– Primary input to the optimization problem

A: rapid increase

B: peer uplink capacity is exhausted

C: downlinks are saturated

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Constrained Optimization Problem

• Coordinator “climbs” each of the curves, always preferring the steepest curve.

• E.g.) The optimal bandwidth allocation for three concurrent swarms.– All the allocation

points have the samederivative.

12

Performance Control and Adaptation

• Provides swarm performance guarantees– Guarantee minimum level of service

– Prioritize swarms

• Updates response curve–When swarm dynamics change

13

Wire Protocol

• Coordinator mints small, unforgeable tokens.• Peers trade each other tokens for blocks.• Peers return spent tokens to the coordinator

as proof of contribution.

14

coordinator

purse ledger

purse ledger

Peer A Peer B

Data block transfer

Performance Comparison

• Antfarm achieves the highest aggregate download bandwidth

15

Swarm Starvation

• Antfarm awards seeder bandwidth to the singleton swarm

16

New Swarm Starvation

• Antfarm achieves an order of magnitude increase in average download speed

17

PlanetLab Experiments

• Response curve

18

• Aggregate bandwidth

Scalability

• Even for large number of peers, the bandwidth consumption at the coordinator is modest.

19

Conclusion

• Antfarm models swarm dynamics and allocates bandwidth optimally.

• Novel hybrid architecture• Simulation and PlanetLab experiment show

that Antfarm outperforms client-server and BitTorrrent

20