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Antfarm: Efficient Content Distribution with Managed Swarms Ryan S. Peterson, Emin Gun Sirer USENIX NSDI 2009 Presented by: John Otto, Hongyu Gao 2009.

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Presentation on theme: "Antfarm: Efficient Content Distribution with Managed Swarms Ryan S. Peterson, Emin Gun Sirer USENIX NSDI 2009 Presented by: John Otto, Hongyu Gao 2009."— Presentation transcript:

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

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

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

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

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

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

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

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

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 9

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 10

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 11

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 allocation points have the same derivative. 12

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

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. 14 coordinator purseledgerpurseledger Peer A Peer B Data block transfer

15 Performance Comparison Antfarm achieves the highest aggregate download bandwidth 15

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

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

18 PlanetLab Experiments Response curve 18 Aggregate bandwidth

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

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 20


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