Antfarm: Efficient Content Distribution with Managed Swarms

1. Antfarm: Efficient Content Distribution with Managed Swarms Ryan S. Peterson, Emin Gun Sirer USENIX NSDI 2009 Presentedby: John Otto, Hongyu Gao 2009. 10. 21. Adapted from the slides of Eunsang Cho

2. Contents • Problem Definition • Antfarm • Peer’s Perspective • Coordinator’s Perspective • Evaluation • Conclusion

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

4. Existing Approaches • Client-server • Pros: simple due to central authority • Cons: cost and scalability

5. Existing Approaches • Peer-to-peer swarms • Pros: reduced cost • Cons: limited information, no control or performance guarantees

6. Goals • High performance • Low cost of deployment • Performance guarantees • Administrator can control over swarm performance • Scalability

7. Antfarm • Hybrid peer-to-peer architecture • Content distribution  optimization problem • Central authority (coordinator) makes decision how to allocate bandwidth optimally.

8. System Overview coordinator seeder • Seeder: trusted servers managed by the coordinator that distribute data blocks to peers. swarm

9. 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

10. 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

11. 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

12. 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 allocationpoints have the samederivative.

13. Performance Control and Adaptation • Provides swarm performance guarantees • Guarantee minimum level of service • Prioritize swarms • Updates response curve • When swarm dynamics change

14. 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. purse ledger purse ledger coordinator Peer A Peer B Data block transfer

15. Performance Comparison • Antfarm achieves the highest aggregate download bandwidth

16. Swarm Starvation • Antfarm awards seeder bandwidth to the singleton swarm

17. New Swarm Starvation • Antfarm achieves an order of magnitude increase in average download speed

18. PlanetLab Experiments • Response curve • Aggregate bandwidth

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

20. Conclusion • Antfarm models swarm dynamics and allocates bandwidth optimally. • Novel hybrid architecture • Simulation and PlanetLab experiment show that Antfarm outperforms client-server and BitTorrrent