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On the Efficiency of Collaborative Caching in ISP-aware P2P Networks

On the Efficiency of Collaborative Caching in ISP-aware P2P Networks. Jie Dai……Hai Jin et al. H.K.U.S.T. U.T. H.U.S.T. IEEE Infocom, Shanghai, China, April 10-15, 2011. Presenter: Su Hu. Warm-up. For the first 4 downloaded pieces , the pieces are selected at random.

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On the Efficiency of Collaborative Caching in ISP-aware P2P Networks

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  1. On the Efficiency of Collaborative Caching inISP-aware P2P Networks Jie Dai……Hai Jin et al. H.K.U.S.T. U.T. H.U.S.T. IEEE Infocom, Shanghai, China, April 10-15, 2011 Presenter: Su Hu

  2. Warm-up Forthefirst4downloadedpieces,the piecesareselectedatrandom P2P: Overlay network

  3. Warm-up P2P Overlay Application data Internet access service ISP Underlay Challenges ISPs Tremendous data volume Costly inter-ISP traffic 1) Not at the same layer

  4. Warm-up Profit local loop, bandwidth definition, ADSL architecture …… Challenges ISPs 2) Users pay for bandwidth, why throttling ? Shared bandwidth

  5. Outline α, q, β, η, ISP index etc. Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  6. Abstract Why Collaborative Cache Reduce the inter-ISP traffic Existing design ignores: Dynamic P2P traffic patterns, ISP peering, cache server capacity …. Analysis of resource allocation with awareness of Inter-ISP traffic and ISP policies

  7. Abstract Our work Characterize inter-ISP traffic patterns Develop cache allocation framework focus on minimizing inter-ISP traffic. Incorporate both locality-aware/unaware & ISP peering agreements The research help us understand Traffic characteristics of existing P2P Design of collaborative ISP cache mechanisms

  8. Outline Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  9. Introduction Proximity-driven biased neighbor select Background 1: The Tussle P2P: 70% of the Internet traffic Can ISP throttle P2P packets? ISP want to maintain customer bases Background 2: How to resolve it Disparity Locality-aware peer selection: P4P TopBT vulnerable due to the dynamic of P2P

  10. Introduction Reduce access latencies to web page Cache for P2P Redirect traffic to cache server at edges of ISP Reduce the latency of P2P packet Our Solution- caching Web cache Collaborative Caching lead to win-win: Inter-ISP Experiences of User

  11. Introduction Both Storage (cache hit ratio) & bandwidth (server’s uploading capacity) constraints are important. The collaboration between ISPs over the public Internet & corresponding cache server New Characteristics from web cache Mitigate the inter-ISP traffic Inter-ISP traffic pattern, collaboration between P2P & ISP Cache server resource allocation ISP peering agreements

  12. Introduction Video distribution platform ISP scales , channel popularity Reduce i-ISP traffic both locality-aware/unaware peer selection Positive on mitigation i-ISP traffic Propose a Optimization framework Theoretical model of i-ISP traffic Resource allocation scheme The effects of ISP peering on our solution Collaborative cache scheme tailored to ISP peering

  13. Outline Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  14. Related Work • 3 classes of ISP-friendly design • Peer-driven PPLive’s latency based mechanism, TCP ping • ISP-driven P4P: ISP advertise preferred paths to P2P app. • Why ISP caching? Not impair the P2P robustness Transparent to end user Upon locality-aware system

  15. Related Work This paper: inter-ISP traffic model, server storage and bandwidth constraints ,peer selection, ISP peering • Existing P2P cache design • Focus on independent server cache • Improving the byte hit ratio • Ignore ISP collaboration & cache server bandwidth constraint • Existing collaborative cache design • Dan’s work: Rate allocation among cache servers Ignore inter-ISP traffic model, practical constraints in real P2P

  16. Outline Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  17. I-ISP Traffic Model & Cache Allocation • Inter-ISP traffic model • P2P video streaming • locality-aware locality-unaware • Optimization framework of allocation resource • Inter-ISP traffic mitigation • Two sets of server strategies • Collaboration between P2P app. & cache server

  18. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model Assume streaming length is same, only depend on streaming rate video channels : number of concurrent users in P2P v system : number of concurrent users in video channel i Assume peer out-degree equals in-degree : streaming rate of video channel i : size of video channel I : in-degree of individual peers • Notation • P2P video streaming

  19. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model : number of ISP in which peers view video ? : Storage capacity by cache server in ISP k : uploading bandwidth by cache server in ISP k : percentage of channel i stored in c server in ISP k : uploading bandwidth to channel i by c server in ISP k : number of concurrent users of channel i in ISP k • Notation • Existing ISPs ISP1 is most popular, ISPk is lest popular

  20. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model Probability that any user view channel i (1) P2P object be accessed over long term: Zipf-Mandelbrot distribution the probability the probability Probability that any user is in ISP k (2) β: different scenarios of ISP user populations • Channel popularity distribution q i • ISP user distribution β = 0, same user amount each ISP higher the β, more unbalanced the ISP user

  21. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model (3) (4) Evenly selected, Neighbors decides mainly by ISP user numbers • Inter-ISP traffic rate model (n-c) 1. Locality-unaware peer selection m：number of neighbor in same ISP Hyper-geometric distribution

  22. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model M defectives in N, extract n samples, and the probability of k defectives p2p streaming server is the external sources. (5) H(n , M , N) p(x=k) = C(k , M) * C(n-k , N-M) / C(n , N) k= max(0 , n-N+M) , …… , min(n , M) N – xi M – xik n – din

  23. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model (6) Inter-ISP generate by channel I in ISP k: 1) more popular channel more inter-ISP traffic 2) ISPs have similar scales, 3) ISPs have widely different scales,

  24. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model Give priority to nearby peer (evaluate by the ISP peer in) i-ISP traffic per peer i-ISP traffic per peer (7) • Inter-ISP traffic rate model (n-c) 2. Locality-aware peer selection ：number of persistent external links

  25. I-ISP Traffic Model & Cache Allocation Inter-ISP traffic model Locality-unaware Locality-aware : 30 : 5-10 • = 80%, both have similar inter-ISP traffic • -> 0 , both coefficients values -> 1 • the left coefficients is always larger than the right

  26. I-ISP Traffic Model & Cache Allocation Cache resource allocation mechanisms Peers in any channel are evenly distributed along the channel ? (8) Minimize Subject to: Maximize • Subject to: ≤ (10) (9) Inter-ISP traffic rate for ISP k:

  27. I-ISP Traffic Model & Cache Allocation Cache resource allocation mechanisms (11) (12) (13) Theorem 1 For max i-ISP mitigation, optimal resource allocation:

  28. Continuous knapsack, solution: Non-decreasing with index Use greedy algorithm , give storage as needed for channel with higher priorities, (11) Achieve upper of as min ( , ) using (12) , (13) I-ISP Traffic Model & Cache Allocation Cache resource allocation mechanisms (14) Theorem 1 Proof: Maximize Subject to:

  29. I-ISP Traffic Model & Cache Allocation Cache resource allocation mechanisms Precisely indentify the content requests of P2P packets needs help of P2P app. Reduce end-to-end latencies, Mitigate i-ISP prevents throttling by ISP Theorem 1 Remark: Design guidelines of collaborative cache mechanism: • P2P system parameters: number of users, channel popularity, file size, streaming rate of channel • ISP cache server needs to collaborate with P2P app.

  30. I-ISP Traffic Model & Cache Allocation Cache resource allocation mechanisms Population-based I, Concurrent users x. Algorithm 1: Optimization-based Collaborative Cache framework for i-ISP mitigation • P2P app. actively transmits system states to ISP cache server. • Compute , , allocate ,, as , • Cache server cut request to external, if average uploading rate to channel , satisfy the request • Monitor P2P states, adjust resource according to T1.

  31. Outline Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  32. Improve Cache with ISP Peering Agreement ISP Peering Agreements Free i-ISP traffic is not need to cache (15) symmetric Matrix E • Concept • ISPs provide free connectivity to transit user • Alleviate costly transit traffic • 2 positive outcomes • Large group of traffic-free candidate neighbor • Strategically select P2P content to store and deliver • ISP peering relation is Reflexive & Symmetric

  33. Improve Cache with ISP Peering Agreement Only peers in peering ISP help to mitigate i-ISP traffic, no collaboration between cache servers Impact of ISP Peering (5) (16) (17) • Not-full collaboration between peering ISPs • Cache server not deliver to peers of peering ISP • Locality-unaware peer selection

  34. Improve Cache with ISP Peering Agreement Compared to (6), here need to also subtract the probability of being peering ISP Impact of ISP Peering (18) i-ISP traffic per peer i-ISP traffic per peer Multiply not (19) • Not-full collaboration between peering ISPs • Locality-unaware peer selection (cont.) • Locality-aware peer selection

  35. Improve Cache with ISP Peering Agreement Impact of ISP Peering (18) (19) • For both scenarios i-ISP traffic reduced due to expansion of free neighbor candidates.

  36. Improve Cache with ISP Peering Agreement Improving cache with ISP Peering Cache server not only serve for peers in own ISP, but also to peering ISPs : bandwidth assigned by to for channel i <------ • Full collaboration between peering ISPs • The bottleneck One ISP’s cache server can’t store whole P2P object -- Cache server bandwidth utilization insufficient • Peering: combine of global cooperative cache • Peering-based full collaboration Upload rate for i rate of i-ISP can be intercept( )

  37. Improve Cache with ISP Peering Agreement Improving cache with ISP Peering Any request to i can be served if sufficient bandwidth (20) Upper bound, Centralized solution, inappropriate for practice Peering, resource, limit aik to serve max , propose a distributed collaborative cache scheme in algor 2 (21) • Full collaboration between peering ISPs Maximize Subject to:

  38. Improve Cache with ISP Peering Agreement Algorithm 2: An ISP Collaboration-based Distributed Cache framework for i-ISP mitigation • Cache server announce surplus bandw and storage to peering ISPs. • After announce of , sorts channel in descending order of ,first channel , , bandw request to • Upon receive r from , allocates and confirm • After confirm of , evicts content confirm, reallocate to such , broadcast surplus info to peering ISP.

  39. Outline Warm-up Abstract Introduction Related Work Inter-ISP Traffic Model & Cache Allocation Improving Cache with ISP Peering Agreement Performance Evaluation Conclusion Summary

  40. Performance Evaluation Trace-Driven Analyses Statistical result of measurement on UUSee: Number of channels: 993 (channel 100 has 100 users at peak time) Number of concurrent users: 100000 To fit the cure of peak time users: • α = 0.78 q = 4 = 30 η = 5 • Evaluation of Inter-ISP Traffic Pattern • Factors: P2P content popularity, ISP popularity • L-A(locality-aware) & L-U(locality-unaware)

  41. Performance Evaluation Evaluation of Inter-ISP Traffic Pattern Fig.1.

  42. Evaluation of Inter-ISP Traffic Pattern Performance Evaluation η Fig.2.

  43. Performance Evaluation Fig.3.

  44. Evaluation of Collaborative Cache Mechanisms Performance Evaluation Fig.4.

  45. Evaluation of Collaborative Cache Mechanisms Performance Evaluation Fig.5.

  46. Evaluation of Collaborative Cache Mechanisms Performance Evaluation Fig.6.

  47. Performance Evaluation Evaluation of ISP Peering Agreements = 10 3 Peering Scenarios 1） Scenario 1: 1/2 3/4 … 9/10 extreme unbalanced • 2） Scenario 2: 1/6 2/7 … 5/10 still has original property • 3） Scenario 3: 1/10 2/9 … 5/6 extreme balanced

  48. Evaluation of ISP Peering Agreements Performance Evaluation Fig.7.

  49. Evaluation of ISP Peering Agreements Performance Evaluation Fig.8.

  50. About percentage of 10 ISPs, so it can’t reach 1 Evaluation of ISP Peering Agreements Performance Evaluation Fig.9.

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