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A Peer-To-Peer Internet Measurement Platform and Its Applications in Content Delivery Networks

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A Peer-To-Peer Internet Measurement Platform and Its Applications in Content Delivery Networks A PEER-TO-PEER INTERNET MEASUREMENT PLATFORM AND ITS APPLICATIONS IN CONTENT DELIVERY NETWORKS BY SIPAT TRIUKOSE Submitted in partial fulfillment of the requirements for the degree of Doctor Of Philosophy DISSERTATION ADVISOR: DR. MICHAEL RABINOVICH DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE CASE WESTERN RESERVE UNIVERSITY JANUARY 2014 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of SIPAT TRIUKOSE candidate for the Doctor of Philosophy degree *. MICHAEL RABINOVICH TEKIN OZSOYOGLU SHUDONG JIN VIRA CHANKONG MARK ALLMAN (date) December 1st, 2010 *We also certify that written approval has been obtained for any proprietary material contained therein. Contents List of Tables . vi List of Figures . ix List of Abbreviations . x Abstract . xi 1 Introduction 1 1.1 Internet Measurements . 1 1.2 Content Delivery Network (CDN) . 4 1.2.1 Akamai and Limelight . 6 1.2.2 Coral . 7 1.3 Outline . 7 1.4 Acknowledgement . 9 2 Related Work 10 2.1 On-demand Network Measurements . 10 2.2 Content Delivery Network (CDN) Research . 12 2.2.1 Performance Assessment . 12 2.2.2 Security . 13 2.2.3 Performance Improvement . 14 3 DipZoom: Peer-to-Peer Internet Measurement Platform 17 3.1 System Overview . 17 i 3.2 The DipZoom Measuring Point (MP) . 21 3.2.1 MP-Loader, MP-Class, and MP Configurations . 25 3.2.2 Authentication . 30 3.2.3 Keep Alive . 37 3.2.4 Measurement . 39 3.3 The DipZoom Client and API . 43 3.4 Security . 44 3.5 Performance . 47 3.5.1 Scalability: Measuring Point Fan-Out . 47 3.5.2 Scalability: Client Fan-Out . 49 3.5.3 Demonstration Experiments . 49 3.6 Conclusion . 54 4 A Large Scale Performance Study of a Commercial CDN 55 4.1 Introduction . 55 4.2 Methodology . 58 4.2.1 Edge Server Discovery . 58 4.2.2 Overriding CDN Edge Server Selection . 60 4.2.3 Controlling Edge Server Caching . 61 4.2.4 Assessing Client-Side Caching Bias . 63 4.2.5 Measuring Edge Server Performance . 65 4.3 Performance of Edge Discovery . 65 4.4 Performance of Akamai CDN . 69 4.4.1 Does a CDN Enhance Performance? . 69 4.4.2 How Good Is Akamai Server Selection? . 73 4.5 Performance of Consolidated Akamai CDN . 76 4.5.1 Data Center Consolidation . 76 4.5.2 Impact of Incomplete Edge Server Discovery . 78 ii 4.5.3 DipZoom Experiment . 79 4.5.4 A Live Study . 81 4.6 Conclusion . 85 5 Security Issues in Commercial CDNs 89 5.1 Introduction . 90 5.2 The Attack Components . 93 5.2.1 Harvesting Edge Servers . 93 5.2.2 Overriding CDN's Edge Server Selection . 94 5.2.3 Penetrating CDN Caching . 95 5.2.4 Amplifying the Attack: Decoupled File Transfers . 98 5.2.5 Verification . 99 5.3 End-to-End Attack . 101 5.3.1 The Setup . 103 5.3.2 A Sustained Attack . 103 5.3.3 A Burst Attack . 104 5.3.4 Discussion: Extrapolation to Commercial CDNs . 105 5.4 Implication for CDN Security . 107 5.5 Mitigation . 108 5.5.1 Defense by Content Provider . 108 5.5.2 Mitigation by CDN . 110 5.6 Conclusion . 111 5.7 Acknowledgement . 112 6 Client-Centric Content Delivery Network 115 6.1 Introduction . 116 6.2 Architectural Approaches . 117 6.3 The Effect of Infrequent Server Selection . 119 iii 6.4 Performance Improvement . 122 6.4.1 Data Set . 123 6.4.2 The Improvement Simulation . 126 6.4.3 The Replay Experiment . 131 6.5 Discussion . 139 6.5.1 Realization of this approach . 139 6.6 Conclusion . 140 6.7 Acknowledgement . 140 7 Conclusion 142 Bibliography . 146 iv List of Tables 3.1 Detail of MP INFO field . 34 3.2 TCP/UDP destination ports summary . 42 3.3 Security threads in DipZoom and counter-measures . 44 4.1 Initial vs. repeat download performance of an object with an appended random search string. 62 4.2 The difference of RTT distance (in milliseconds) from clients to the nearest data center in a given consolidated platform and to the Akamai- selected server in the current platform (live clients). 82 5.1 The throughput of a cached object download (KB/s). Object requests have no appended random string. 96 5.2 Initial vs. repeat download throughput for Akamai (KB/s). Requests include appended random strings. 97 5.3 Initial vs. repeat download throughput for Limelight (KB/s). Requests include appended random strings. 98 5.4 The download throughput (KB/s) of the monitor client. The monitor request is sent 0.5s after the probing request. 101 5.5 Average traffic increase during the attack period. 104 6.1 Pearson correlation of all trial pairs . 122 v 6.2 Performance of the client-centric CDN vs the current practice (best case scenario) . 128 6.3 Replay times (in second) of ext-our-replay and ext-current-replay. 135 6.4 Replay times (in second) of int-our-replay and int-current-replay. 135 6.5 TCP connection utilization in the network with no local Akamai edge server deployed. 135 6.6 TCP connection utilization in the network with a local Akamai edge server deployed. 136 vi List of Figures 1.1 Content delivery network . 5 3.1 DipZoom measuring point overview . 21 3.2 Measuring thread on measuring point (MP) . 24 3.3 An example of MP configuration file dipzoom mp.conf in XML format 26 3.4 Custom measurement plug-in management system: login screen . 30 3.5 Custom measurement plug-in management system: new measurement sign-up . 31 3.6 Custom measurement plug-in management system: available measure- ment list . 32 3.7 Custom measurement plug-in management system: MP configuration tool..................................... 33 3.8 Custom measurement plug-in management system: generated MP con- figuration . 34 3.9 Custom measurement plug-in management system: preference . 35 3.10 Burst MP logins test . 49 3.11 Average MP login successes . 50 3.12 The sustained rate of MP operations involved in a measurement . 51 3.13 Classified King/DipZoom ratios . 52 4.1 Active DipZoom measurement points on 5/09/09. 59 vii 4.2 Relation between download throughput and RTT. 63 4.3 Cumulative Akamai's edge server discovery against CNAMEs . 65 4.4 Akamai's edge servers discovery . 66 4.5 Progressive discovery of Akamai edge servers with time . 67 4.6 The performance benefits of Akamai delivery. 70 4.7 The residential client performance benefits of Akamai delivery. 71 4.8 The comparison of no-cache download through Akamai and download from origin server. 72 4.9 The fraction of servers outperformed by the Akamai-selected server. 74 4.10 Download throughput difference between Akamai-selected and an al- ternative edge server . 75 4.11 The implication of incomplete platform discovery: A client may be redirected to a more distant location. 78 4.12 The performance of a consolidated Akamai platform with different number of data centers. 80 4.13 The performance of a consolidated Akamai platform with different tar- get object size. 86 4.14 The performance of a consolidated Akamai platform with different download speed. 87 4.15 The performance of a consolidated Akamai platform for residential speed links. 88 5.1.
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