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P4P: Proactive Provider Assistance for P2P

P4P: Proactive Provider Assistance for P2P. Haiyong Xie (Yale) haiyong.xie@yale.edu. *This is a joint work with Arvind Krishnamurthy (UWashington) and Richard Yang (Yale). Roadmap. Motivation P4P framework Design rationale System architecture

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P4P: Proactive Provider Assistance for P2P

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  1. P4P: Proactive Provider Assistance for P2P Haiyong Xie (Yale) haiyong.xie@yale.edu *This is a joint work with Arvind Krishnamurthy (UWashington) and Richard Yang (Yale).

  2. Roadmap • Motivation • P4P framework • Design rationale • System architecture • Computing peering suggestions • Evaluations • Conclusion and future work 1st NYC P2P Workshop

  3. http://www.cachelogic.com P2P : The Significant Bandwidth Consumer • Up to 60-70% of Internet traffic is contributed by P2P applications [cachelogic] • Problems • Scattered traffic • Increased network utilization • Degraded performance of other applications • Increased network operational costs 1st NYC P2P Workshop

  4. ISPs try to “manage” P2P traffic P2P tries to evade from being captured • Upgrade network infrastructure • Deploy P2P caching devices • Rate limit P2P traffic • Use random ports • Encrypt traffic Bandwidth Battle between ISPs and P2P • The battle results in a lose-lose situation • ISPs: increased financial and operational costs, increased network utilization, degraded performance • P2P: increased complexity of P2P applications, reduced interoperability, and impeded development of P2P applications 1st NYC P2P Workshop

  5. Design a framework so that ISPs and P2P can work together to achieve better results Objective: • Where are the problems? • ISPs do not disclose their network information for privacy concerns • P2P does not have sufficient information to determine network-aware peering relationships • ISPs can expose information to “guide” the peering relationships in P2P systems to • Improve the throughput of P2P users • Lower traffic costs for ISPs, balance traffic across the network 1st NYC P2P Workshop

  6. P4P Framework – Design Rationale • Scalability • Support a large number of P2P users and networks in dynamic settings • Privacy preservation • Try to improve performance for both ISPs and P2P • Extensibility • Application-specific requirements • Tracker-based vs. trackerless P2P systems • Gossip among peers • Incremental deploymentability 1st NYC P2P Workshop

  7. pTracker iTracker 2 3 4 1 ISP A peer Design For Tracker-based P2P • Use BitTorrent in a single ISP as an example • pTracker keeps P2P system states • iTracker makes suggestions for peering relationships • Information flow: • 1. peer queries pTracker • 2. pTracker asks iTracker for guidance • 3. iTracker returns high-level peering suggestions • 4. pTracker selects and returns a set of active peers, according to the suggestions iTracker can be run by third parties trusted by ISPs. 1st NYC P2P Workshop

  8. Service Interfaces and States • Services provided by iTracker • Map an IP address to an opaque, privacy-preserving PID PID = getpid(ip) • Compute peering suggestions for a given PID-based P2P state [wij] = getpeering(PID-based-state) wij: probability with which peers of PID i establish peering relationship with peers of PID j • pTracker keeps states • PID-peer state (updated by calling getpid() interface call) • P2P state (updated by collecting peer information) 1st NYC P2P Workshop

  9. How to Use iTracker Services • When a new peer joins the P2P network and queries the pTracker • pTracker gets the PID for this peer by calling getpid() • pTracker updates its internal P2P state • pTracker gets the PID-based peering suggestion [wij]by callinggetpeering() • pTracker selects a set of active peers according to [wij] and returns it • [wij]can be used torepresent the peering relationships among peers, and can be used to analyze performance • Original BitTorrent protocol selects peers randomly: wij = nj / ∑nk • BitTorrent through caching (each PID has a caching peer only, and the remaining peers in the same PID peers with the cache): wij = 0 for non-caching peers and i≠j 1st NYC P2P Workshop

  10. Pros and Cons • Evaluate the design • Pros • iTracker is stateless • Need no modification to P2P clients • Preserve privacy • Cons • Cannot handle trackerless P2P systems • Cannot handle gossip 1st NYC P2P Workshop

  11. iTracker 2 2 pTracker 1 1 ISP A Peer a Peer b 3 The Complete Design • iTracker’s responsibilities • Keeps P2P system states (PID-based, light-weight) • makes suggestions for peering relationships • Information flow: • 1. peers register or update with iTracker • 2. iTracker returns PID and PID-based peering suggestions • 3. Peers exchange peer information (with associated PID information) through gossips • 4. Peers update peering relationships according to the received peering suggestions 1st NYC P2P Workshop

  12. How iTracker Works – Computing Peering Suggestions • ISP’s model • ISP’s network is a graph G=(V,E) • Link utilization be due to background traffic on edge e • Ie(i,j)=1 iff edge e is on the route from node i to j, determined by ISP’s routing scheme • P2P’s model • There are K P2P systems • Uploading/downloading capacity for all peers with PID i: uik, dik • Traffic uploaded from PID-i peers to PID-j peers: tijk • Peering suggestion • Allow a certain number of random connections to ensure robustness 1st NYC P2P Workshop

  13. How iTracker Works – Computing Peering Suggestions (cont’d) • Formulate as a joint optimization problem • ISP’s objective: minimize maximum link utilization • P2P’s objective: maximize throughput • Joint optimization: min max link utilization for ISP when P2P throughput is maximized 1st NYC P2P Workshop

  14. How iTracker Works – Computing Peering Suggestions (cont’d) • Naïve approach takes multiple steps • Get optimal throughput Toptk for each P2P system k by solving its corresponding optimization problem individually • Solve the ISP optimization problem with constraints of each P2P system’s throughput being maximized • One-step approach through duality transformation • Apply dual transformation to P2P optimization problem • Obtain a new optimization problem by merging ISP and dual P2P problems 1st NYC P2P Workshop

  15. Roadmap • Motivation • P4P framework • Design rationale • System architecture • Computing peering suggestions • Evaluations • Conclusion and future work 1st NYC P2P Workshop

  16. Evaluation – Methodology • Simulations • Discrete-event simulation • a module for modeling BitTorrent protocol • a module for modeling underlying network topology and data transfer dynamics using TCP rate equation • Network topology: PoP-level AT&T and Abilene topologies • Network routing: OSPF routing • PlanetLab experiments • 53 Internet2 nodes on PlanetLab • iTracker for Abilene network • Use OSPF routing to re-construct traffic load on Abilene links 1st NYC P2P Workshop

  17. Evaluation – Abilene Simulation • Compared to P4P, native P2P can result in • 2x download completion time • 2x higher link utilization • Native P2P can result in some peers experiencing very long download completion time • Native P2P can result in much larger variance in link utilization 1st NYC P2P Workshop

  18. Evaluation – AT&T Simulation • Compared to P4P, native P2P can result in • 1.6x download completion time • 3x higher link utilization • Some peers can experience very long download completion time with native P2P • Link utilization variance can be larger for native P2P 1st NYC P2P Workshop

  19. Evaluation – Liveswarms on Planetlab • Liveswarms* is a P2P-based video streaming application, which adapts BitTorrent protocol to video streaming context • Run liveswarms on 53 PlanetLab nodes for 900 seconds • P4P and native liveswarms achieve roughly the same amount of throughput • P4P reduces link load • Average link load saving is 34MB • Maximum average link load saving is 60% • Native liveswarms:1Mbps • P4P liveswarms: 432Kbps *Michael Piatek, Colin Dixon, Arvind Krishnamurthy, Tom Anderson. LiveSwarms: Adapting BitTorrent for end host multicast. Technical report: UW-CSE-06-11-01 1st NYC P2P Workshop

  20. Conclusion and Future Work • Our design achieves the objective • Scalability: iTracker is light-weight, maintains necessary states only • Privacy preservation • Extensibility • Robustness • Performance improvement for both ISPs and P2P • Incremental deploymentability • Implementation • Incentives 1st NYC P2P Workshop

  21. Questions? 1st NYC P2P Workshop

  22. Backup Slides 1st NYC P2P Workshop

  23. Computation cost is low • Among 34K+ swarms, <1% have more than 100 leechers, and about1% swarms contribute to 50% of traffic demand • Solution time of the optimization problem is linear to number of swarms (slope ≈ 0.025) 1st NYC P2P Workshop

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