Transcription

1

2

3 ABSTRACT

4

5 Acknowledgments

6

7 List of Abbreviations

8

9 Contents ABSTRACT 3 Acknowledgments 5 List of Abbreviations 7 List of Figures 15 List of Tables 23 1 Introduction 25 2 Motivation and background 29 3 Overview of dissertation work 33

10 users CDN CDN ISP Users ISP 4 User-CDN tension: remote DNS use 39

11 5 CDN-ISP tension: CDN model and traffic diversity 71

12 6 ISP-User tension: cross-isp P2P traffic 99

13

14 7 Contributions and Conclusions 141 References 143

15 List of Figures N <

16 . r =. ECS

17 Direct Resolution DR > DR namehelp

18 >

19

20 T T U V T T > T T

21

22

23 List of Tables

24

25 Chapter 1 Introduction 1.1 Trends and Tensions

26 The growth in use of remote DNS services creates tension between users and CDNs. Increasing CDN market diversity results in friction between CDNs and ISPs. BitTorrent, the largest P2P content distribution system, causes stress between users and ISPs. 1.2 Roadmap

27

28

29 Chapter 2 Motivation and background 2.1 Growth in content

30 2.2 Content distribution approaches > Infrastructure-based CDNs

31 % using CDNs N most popular sites Sites Pageviews N N Infrastructure-less P2P systems

32 2.3 Summary

33 Chapter 3 Overview of dissertation work 3.1 Key perspectives in content delivery Users and content providers user

34 3.1.2 CDNs Redirection policies at scale Eyeball ISPs ISPs 3.2 Dissertation argument This dissertation argues that it is possible to identify technical solutions that alleviate the tensions between users, CDNs and ISPs by sharing readily available information between them.

35 3.2.1 Economic and technical duality technical economic Scope of dissertation work I focus on the current incarnation of the Internet. The technical solutions I find may not be optimal.

36 It may not be possible to prove this argument in a general way. 3.3 Trends and tensions between players Use of remote DNS by users to locate CDN content Ch Diverse CDN traffic patterns on ISP networks Ch Users running P2P systems over ISP networks Ch. 6

37 3.4 Methodology: empirical approach Leveraging Ono, NEWS, Dasu and Namehelp platforms

38 3.4.2 Aggregating end-host vantage points

39 Chapter 4 User-CDN tension: remote DNS use

40 4.1 Trend: growth in remote DNS use Any

41 % of users Any OpenDNS Google Level3 May 2010 Jul 2010 Sep 2010 Nov 2010 Jan 2011 Mar 2011 May 2011 Jul 2011 Sep 2011 Nov Trend: industry response, edns-client-subnet DNS extension ECS

42 both the DNS service and the CDN ECS both the DNS service and CDN must support the extension only public service no ISP DNS services N ECS

43 % of sites No CDNs support ECS Some CDNs support ECS N most popular sites ECS

44 4.3 Methodology

45 < best case results Obtaining CDN redirections actual network location

46 and DNS extension supported. is supported ECS ECS ECS DNS extension not supported. do not ECS

47 4.3.2 Measuring DNS services unicast IP address separate unicast interface Filtering configured public DNS services. unicast IP address

48 Send DNS query Get DNS answer; HTTP connect Send request Rec'd headers Rec'd first byte of object Transfer complete DNS latency HTTP latency End-to-end latency Measuring CDNs the first byte GET Baseline for performance comparison

49 baseline performance best for each location any 4.4 Tension: user DNS choice vs. CDN performance

50 CCDF of ISPs Failure Rate (%) CCDF of ISPs sec 1 min 1 hr 1 day Mean Time to Repair DNS Service p(failure) MTTR < < ISPs 0.8% 10 < Benefits of public DNS services

51 < Performance implications of public DNS services Impact on CDN redirections

52 Iterative-Iterative Iterative-ISP Iterative-Google Iterative-OpenDNS Iterative-Iterative Iterative-ISP Iterative-Google Iterative-OpenDNS CDF 0.4 CDF Cosine Similarity Cosine Similarity cosine similarity A B [, ] cos sim = A B A B cos sim = cos sim =

53 at least <

54 Google DNS OpenDNS CDF of locations Network latency % difference, Public vs ISP DNS twice as far away potential < necessary but not sufficient Impact on HTTP performance

55 CDF Iterative ISP DNS Google DNS OpenDNS HTTP latency % difference CDF Iterative ISP DNS Google DNS OpenDNS HTTP latency % difference Cause of performance impact

56 CDF of locations Iterative ISP DNS Google DNS OpenDNS Fraction of HTTP time waiting for header and start of object.

57 500 Ping latency (ms) HTTP latency (ms) r =. r =. n = 4.5 Solution: share user location with CDNs DR DR namehelp DR Better CDN performance using client location ECS

58 CDF of locations ECS Whole IP ECS /16 Prefix ECS /24 Prefix HTTP latency % difference ECS ECS ECS ECS

59 CDF of locations CDF of locations ISP DNS Google DNS Google DNS with ECS End-to-end latency % difference ISP DNS Google DNS Google DNS with ECS End-to-end latency % difference For these CDNs like deployments more ECS

60 ECS ECS ECS ECS ECS ECS Direct Resolution approach Direct Resolution DR DR DR Direct Resolution DR DR

61 Query Client Recursive DNS Authoritative DNS 1 Lookup hostname Have CNAME mapping to CDN hostname Resolve hostname Select CDN server for recursive DNS 2 Lookup nameserver for CDN hostname Have address of CDN authoritative DNS server Resolve nameserver for CDN hostname 3 Directly request CDN redirection Have CDN redirection for client's location Select CDN server for client Direct Resolution directly contacts the CDN s authoritative server CDN redirections using Direct Resolution DR DR location of the recursive DNS server

62 4.5.3 Performance evaluation DR how DR DR end-to-end performance DR

63 CDF Google DNS DR DR (cached) End-to-end latency % difference CDF Google DNS DR DR (cached) End-to-end latency % difference DR DR DR DR DR DR DR

64 CDF Google DNS DR (optimized) Google DNS with ECS End-to-end latency % difference > DR DR optimized DR optimized DR optimized DR optimized DR optimized DR Optimized DR DR

65 optimized DR Namehelp platform and deployment DR namehelp namehelp DNS proxy daemon DR namehelp namehelp namehelp namehelp DR namehelp Performance comparison utility namehelp namebench namebench namehelp

66 Receive request Query recursive server N In local cache? Y Return cached response Y Is a CDN? N Should do DR? N Do probabilistic asynchronous DR Return original response Y Cached NS? N Do asynchronous NS lookup Y Do DR: query NS directly Do asynchronous comparison: DR vs. recursive Return modified response namehelp namehelp namebench namehelp overall time comparable

67 namebench Deployment namehelp namehelp 4.6 Related Work CDNs

68 4.6.2 DNS and CDNs Solutions

69 edns-clientsubnet unmodified applications Direct Resolution namehelp DR 4.7 Summary and Contributions Direct

70 Resolution namehelp namehelp namehelp

71 Chapter 5 CDN-ISP tension: CDN model and traffic diversity 5.1 Trend: diversification of content delivery approaches

72 Location on-network functionality dedicated links shared links origin server Distance D0 D1 D2 D3 near Distance from end users far D3 D2 D1 PoPs External Networks ISP D0 Cities CDNs using shared links Directly-connected CDNs,

73 5.1.3 Peer-to-peer CDNs and ISP on-network functionality Summary 5.2 Methodology

74 5.2.1 Datasets undns ISP dataset of netflow records

75 End-user dataset of CDN performance

76 5.2.2 Metrics End-users ISP content providers CDNs End-users CDNs magnitude percent change in load

77 ISPs

78 Non-considerations Emulating content locations city-level conservation of traffic

79 5.3 Tension: CDN traffic diversity vs. ISP traffic management Users benefit from nearby content

80 CDF of cities D0 in ISP network D1 dedicated links D2 shared links D3 origin server RTT (ms) CDF of cities D1 dedicated links D2 shared links D3 origin server Standard deviation of RTT (ms)

81 CDF of cities D0 in ISP network D1 dedicated links D2 shared links D3 origin server RTT (ms) CDF of cities D1 dedicated links D2 shared links D3 origin server Standard deviation of RTT (ms)

82 5.3.2 Nearby content challenges CDNs with high traffic variability

83 CDF of aggregation points D2 ISP link D1 ISP PoP D0 City CDN traffic variation (%) CDF of aggregation points D2 ISP link D1 ISP PoP D0 City CDN traffic variation (%) CDF of aggregation points D2 ISP link D1 ISP PoP D0 City CDN traffic variation (%)

84 significantly more Nearby content is preferable for ISPs

85 CDF of links % difference in traffic variation External link traffic

86 40 % difference in traffic variation CDN fraction of link traffic (%)

87 CDN fraction of link traffic (%) > 5% +/- 5% < -5% Normalized link traffic volume (%) > >

88 CDF of cities constrain to 1 PoP % increase in traffic matrix predictability Internal network traffic

89 CDF of links D2 existing shared links D1 new dedicated links Traffic variation (%)

90 5.4 Solution: cooperate to increase predictability and handle traffic bursts Constraining ingress. In-network caching functionality Finding a compromise

91 D1 original D1, 1 PoP D1, 1 PoP D0, city caching, 1 PoP D0, city caching, 2 PoPs D1 original D0, city caching, 2 PoPs Traffic variation (%) Traffic variation (%) Traffic variation (%) Normalized link traffic volume (%) Normalized link traffic volume (%) Normalized link traffic volume (%)

92 CDF of cities constrain to 1 PoP 0.1 constrain to 1 PoP; caching constrain to 2 PoPs; caching % increase in traffic matrix predictability

93 CDF of cities PoPs 3 PoPs 4 PoPs 5+ PoPs % increase in traffic matrix predictability Generalizing to other ISP architectures

94 5.5 Related Work Network-integrated CDNs

95 5.5.2 CDN and ISP federations

96 5.5.3 Peer-assisted content distribution 5.6 Summary and Contributions

97

98

99 Chapter 6 ISP-User tension: cross-isp P2P traffic

100 6.1 BitTorrent dataset Sampling methodology

101 6.2 Towards a representative view of BitTorrent

102 EU 52% EU 52% AF 2% OC 3% SA 4% AS 23% NA 20% AF 2% OC 3% SA 4% NA 16% AS 19%

103 µ 6.3 Trend: growth and shifts in BitTorrent use

104 Hourly peer traffic volume (MB) Hourly peer traffic volume (MB) Growth in per-user BitTorrent traffic Increasingly diurnal usage patterns

105 Avg Hourly Peers Seen Sat Sun Mon Tue Wed Thu Fri Avg Hourly Peers Seen Tue Wed Thu Fri Sat Sun Avg Hourly Peers Seen Fri Sat Sun Mon Tue Wed All EU NA AS SA OC AF Peak:Trough Ratio, Hourly Peers EU NA AS SA OC AF Connected Peer Continent

106 CDF min 1 hr 2 hrs 4 hrs Session duration 12 hrs 1 day days Greater diversity in session time distribution

107 Session Duration (hours) '08 Q3 Q2 Q1 '12 1 All EU NA AS SA OC AF Vantage Point Location All, Q2 increased Growth in system-wide traffic volume

108 / +27 peer download rate (A) concurrent flows (C) per-flow download rate (D) total flows (E) unique peers per hour (B) concurrent flows (C) total download rate (F) per-flow download rate (D) total flows (E) Shift toward diurnal usage

109 Normalized traffic (%) Local hour of day Normalized traffic (%) Local hour of day Normalized traffic (%) Local hour of day Normalized traffic (%) Local hour of day Normalized traffic (%) Local hour of day Normalized traffic (%) Local hour of day

110 6.4 Tension: user access to content vs. ISP costs

111 CDF [X x] BGP + Traceroute BGP Proportion of Mapped Traffic Mapping BitTorrent flows

112 > Where traffic flows where Tier-based topology classification

113 Most traffic stays in lower-tier networks T CDF [X x] Tier 1 Tier 2 Tier 3 Tier Proportion of Traffic T

114 CDF [X x] Tier 1 Tier Proportion of Traffic CDF [X x] Tier 1 Tier Proportion of Traffic CDF [X x] Tier 1 Tier Proportion of Traffic T = U = T = U = T = U = CDF [X x] Tier 1 Tier 2 Tier Proportion of Traffic CDF [X x] Tier 1 Tier 2 Tier Proportion of Traffic CDF [X x] Tier 1 Tier 2 Tier 3 Tier Proportion of Traffic T = U = T = U = T = U = T U V Origin and destination tier affect the tier traffic reaches T U V V T does not go above tier-3

115 stays in tier Economic impact study on ISPs all

116 CDF [X x] Customer Peer Provider Proportion of Traffic CDF [X x] Customer Peer Provider Proportion of Traffic CDF [X x] Customer Peer Provider Proportion of Traffic T Portion of charging traffic

117 CDF [X x] Tier 2 Tier 3 Tier Customer : Provider ratio T > Traffic ratios

118 CDF [X x] Tier 1 Tier 2 Tier 3 Tier (Customer - Provider) / Total >

119 CDF [X x] Tier 2 Tier 3 Tier Customer - Provider (GB) T >

120 Summary of variable costs analysis th-percentile cost contributions of BitTorrent traffic temporal pattern whether BitTorrent is relatively more expensive th-percentile billing and Shapley Value average marginal cost contribution

121 v th m = v th (BT) m = v th (Other + BT) v th (Other) SV BT = (m + m )/ efficient p BT = SV BT /(SV BT + SV Other ) f BT BT = p BT /f BT > comparatively more expensive < comparatively less expensive

122 Impact on 95th-percentile transit costs relatively more expensive T T T T Relative Cost metric and scaling more

123 relative Relative impact of BitTorrent on 95th-percentile costs X X more T

124 Relative Cost Relative Cost Relative Cost Relative Cost BT % of Total Traffic BT % of Total Traffic BT % of Total Traffic BT % of Total Traffic T T T T T later T

125 6.4.5 Summary 6.5 Solution: leveraging available locality in swarms

126 6.5.1 BitTorrent peer discovery mechanisms

127 Random sampling in peer discovery randomly sampled BitTorrent swarm membership dataset under-report

128 Total swarm size /10 06 UTC time % peers in AS5089 (GB) /10 06 Local time Available locality in swarms

129 Peers per network Peak Hour Average Minimum Percent of networks Potential increase in local peer availability

130 < local peer discovery Predicting future local peer availability the future distribution Maximizing tracker-based peer discovery

131 Meta search engines 1 Search engines 2 Trackers Peers Survey of torrent and tracker listings

132 CDF of torrents median maximum Fraction of tracker domains seen per search engine Search Engine A c d b Trackers e a g f Search Engine B Improving peer discovery by using more trackers

133 CDF of torrents minimum median maximum % increase in available swarm population components

134 CDF of torrents minimum median maximum % increase in number of trackers Pushing trackers to the limit

135 CDF of trackers default maximum Number of peers returned per request CDF of trackers m 15m 30m 1h 3h 6h 12h Tracker-specified interval I was able to obtain swarm samples every few seconds from all trackers 140x faster

136 6.5.5 Summary 6.6 Related Work Economic implications of P2P traffic

137 6.6.2 Characterizing P2P traffic locality Biased neighbor selection techniques

138 6.7 Summary and Contributions

139

140

141 Chapter 7 Contributions and Conclusions

142 7.1 Open research directions and ongoing projects

143 References Proc. of IEEE INFOCOM Proc. of IMC SIGCOMM Comput. Commun. Rev. Proc. of the WWW Proc. of IMC Proc. of ICDCS Proc. of ACM CoNEXT Proc. of ACM SIGCOMM Proc. of ACM SIGCOMM

144 Proc. of IEEE INFOCOM Proc. of IMC Proc. of ACM CoNEXT Proc. of PAM ACM SIGCOMM CCR Proc. of IMC ACM CCR Proc. of IEEE INFOCOM Proc. of PAM Proc. of SIGMETRICS/Performance

145 Computer Communications IEEE/ACM Transactions on Networking Proc. of IMC Proc. of IEEE Global Internet Symposium Proc. of ACM CoNEXT Proc. of IMC Workshop Proc. of IMC Proc. of IMC Proc. of ACM SIGCOMM Computer Networks Computer Networks Proc. ACM IMW Proc. of ACM SIGCOMM Proc. of ACM SIGCOMM Proc. of IMC

146 Proc. of USENIX ATC Proc. of ACM SIGCOMM namebench ACM SIGOPS Operating Systems Review Proc. of ACM SIGCOMM Proc. of IMC IEEE J.Sel. A. Commun. Proc. of USENIX OSDI Proc. of HotNets Proc. of IPTPS Proc. of ACM SIGCOMM

147 Proc. of ACM SIGCOMM Proc. of USENIX NSDI Proc. of IMC Proc. of IEEE INFOCOM Proc. of ACM SIGMETRICS Proc. of IMC Proc. of IMC Proc. of ICDCS

148 Proc. of ACM SIGMETRICS Proc. of the WWW Proc. of ACM SIGMETRICS Networking (2) Proc. of IPTPS Proc. of USENIX OSDI CNET News CNET News In Proc. of IEEE GLOBECOM

149 Proc. of ACM SIGCOMM Proc. of IEEE INFOCOM IEEE Transaction on Parallel and Distributed Systems Proc. of IMC

150 namehelp cum laude