Antfarm: Efficient Content Distribution with Managed Swarms Ryan S. Peterson, Emin Gun Sirer USENIX...
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Transcript of Antfarm: Efficient Content Distribution with Managed Swarms Ryan S. Peterson, Emin Gun Sirer USENIX...
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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
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Contents
• Problem Definition• Antfarm– Peer’s Perspective
– Coordinator’s Perspective
• Evaluation• Conclusion
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Problem Definition
• To find an efficient way to disseminate a large set of files to a potentially very large set of clients
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Existing Approaches
• Client-server– Pros: simple due to central authority
– Cons: cost and scalability
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Existing Approaches
• Peer-to-peer swarms– Pros: reduced cost
– Cons: limited information, no control or performance guarantees
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Goals
• High performance• Low cost of deployment• Performance guarantees– Administrator can control over swarm
performance
• Scalability
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Antfarm
• Hybrid peer-to-peer architecture
• Content distribution optimization problem– Central authority (coordinator) makes decision
how to allocate bandwidth optimally.
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System Overview
• Seeder: trusted servers managed by the coordinator that distribute data blocks to peers.
seeder
coordinator
swarm
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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
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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
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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.
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Performance Control and Adaptation
• Provides swarm performance guarantees– Guarantee minimum level of service
– Prioritize swarms
• Updates response curve–When swarm dynamics change
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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.
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coordinator
purse ledger
purse ledger
Peer A Peer B
Data block transfer
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Performance Comparison
• Antfarm achieves the highest aggregate download bandwidth
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Swarm Starvation
• Antfarm awards seeder bandwidth to the singleton swarm
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New Swarm Starvation
• Antfarm achieves an order of magnitude increase in average download speed
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PlanetLab Experiments
• Response curve
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• Aggregate bandwidth
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Scalability
• Even for large number of peers, the bandwidth consumption at the coordinator is modest.
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Conclusion
• Antfarm models swarm dynamics and allocates bandwidth optimally.
• Novel hybrid architecture• Simulation and PlanetLab experiment show
that Antfarm outperforms client-server and BitTorrrent
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