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Multi-path Routing

This presentation explores the benefits of multihoming in network routing, including improved performance and reliability. It analyzes data collected from high-volume content providers and enterprises that receive data, as well as the impact of choosing the right set of providers. The study also compares the performance of different multihoming techniques and emphasizes the importance of careful provider selection.

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Multi-path Routing

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  1. Multi-path Routing CSE 525 Course Presentation Dhanashri Kelkar Department of Computer Science and Engineering OGI School of Science and Engineering

  2. Multi-path Routing • A. Akella, B. Maggs, S. Seshan, A. Shaikh, R. Sitaraman, "A Measurement-Based Analysis of Multihoming", ACM SIGCOMM 2003. • D. Andersen, A. Snoeren, H. Balakrishnan, "Best-Path v. Multi-Path Overlay Routing", IMC 2003. Dhanashri Kelkar – OGI School of Science and Engineering

  3. Multihoming Advantages – The Gist • A study of multihoming performance and reliability • Data collected from Akamai content distribution network • High-volume content providers • Enterprises that mainly receive data • Analysis: • Improve performance and reliability • Choosing right set of providers important Dhanashri Kelkar – OGI School of Science and Engineering

  4. Multihoming • Technique to achieve resilience to service interruptions • Customer network having more than one external link, either to single ISP or to different providers • Mainly used for reliability Dhanashri Kelkar – OGI School of Science and Engineering

  5. K-Multihoming • Customer network multihomed to K (K≥2) service providers • Expect incremental performance Dhanashri Kelkar – OGI School of Science and Engineering

  6. Multihoming – Two Models • Enterprise perspective: • Route data being downloaded through appropriate ISP • Web server perspective: • Route data being provided through appropriate ISP • Does smart routing improve performance? • Does choice of ISPs matter? Dhanashri Kelkar – OGI School of Science and Engineering

  7. Data set A1 27 monitoring nodes Two nodes per city connected to different ISP Every 6 min. nodes download objects from Akamai customers Log turnaround time for request Akamai Customer ISP2 ISP1 Monitor Monitor 1 2 Enterprise Stand-in Data Collection – Enterprise Perspective2-Multihoming Dhanashri Kelkar – OGI School of Science and Engineering

  8. Data set H1 Multiple Akamai servers per city Each server connected to different ISP Servers download from customers periodically Log avg turnaround time each hour Data Collection – Enterprise PerspectiveK-Multihoming (K>2) Dhanashri Kelkar – OGI School of Science and Engineering

  9. Performance – 2-multihoming • Use best provider for each download instead of single provider for all downloads • Performance metric: • Measures how much each ISP loses compared to multihoming solution (≥1) Dhanashri Kelkar – OGI School of Science and Engineering

  10. Performance – K-Multihoming • Performance metric: • particular K-multihoming solution • Best multihoming obtained if we choose best of all ISPs Dhanashri Kelkar – OGI School of Science and Engineering

  11. Enterprise 2-Multihoming: Results • 2-multihoming shows performance benefits but to varying degrees Dhanashri Kelkar – OGI School of Science and Engineering

  12. EnterpriseK-Multihoming Performance • Each line represents different city • No significant improvement after 4 or 5 • Knowing best ISP in advance is important Dhanashri Kelkar – OGI School of Science and Engineering

  13. Data Collection – Web Server Perspective Dhanashri Kelkar – OGI School of Science and Engineering

  14. Web Server Perspective – Cont’d • Data set A2: • In 5 metro areas, pick servers attached to distinct upstream ISPs • Every 6 min. each server downloads 50 KB object from other Akamai servers • Turnaround time for request Dhanashri Kelkar – OGI School of Science and Engineering

  15. Web Server K-Multihoming • Use Akamai servers to emulate multihomed data centers and their active clients • Metric for comparison: same as with enterprises • Not much benefit beyond K=4 Dhanashri Kelkar – OGI School of Science and Engineering

  16. Reliability • Data set containing traceroute measurements from nodes of keynote systems to Akamai servers • 50 geographically diverse keynote nodes, 2 per city • 20 Akamai servers per city (top 20 ISP) • Information about IP-level connectivity • Robustness to IP-level failures Dhanashri Kelkar – OGI School of Science and Engineering

  17. Reliability Metrics • Fraction of total path diversity captured by solution • Higher value shows better performance • Degree of overlap in paths • Lower value shows better performance Dhanashri Kelkar – OGI School of Science and Engineering

  18. Reliability Analysis • For both metrics, significant difference in optimal, average, and worse solution • Difference about 80% • Choosing ISPs very crucial Dhanashri Kelkar – OGI School of Science and Engineering

  19. Conclusion • Multihoming helps, at least 20% improvement on average • But not much beyond 4 providers • Careful choice necessary • Cannot just pick top individual performers • Poor choice can affect performance significantly Dhanashri Kelkar – OGI School of Science and Engineering

  20. Best-path vs. Multi-path Routing • Analysis of performance of reactive and mesh routing • Reactive routing: measure path quality using probes and send on best path • Mesh routing: send redundant duplicates Dhanashri Kelkar – OGI School of Science and Engineering

  21. Design • Probe-based reactive overlay routing • Periodic probes for availability, latency, loss rate • Best path performance • Redundant multi-path routing • Sends redundant data to multiple paths • Path independence Dhanashri Kelkar – OGI School of Science and Engineering

  22. Routing Methods • Direct Single packet, direct path • Direct direct 2 packets, direct, no spacing • DD 10ms 2 packets, direct, 10ms spacing • DD 20ms 2 packets, direct, 20ms spacing • Lat Reactive routing, min latency • Loss Reactive routing, min loss • Direct Rand 2 pkts, Redundant routing • Lat Loss 2 pkts, Redundant multi-path Dhanashri Kelkar – OGI School of Science and Engineering

  23. Duplication Reduces Loss Rate • Type Loss % • direct 0.42 • direct direct 0.30 • dd 10ms 0.27 • dd 20ms 0.27 • Lat 0.43 • Loss 0.33 • Direct Rand 0.26 • Lat Loss 0.23 Dhanashri Kelkar – OGI School of Science and Engineering

  24. Measurement Summary • Redundant beats reactive for low loss • Reactive finds specific good paths • Latency improvements • Low loss paths Dhanashri Kelkar – OGI School of Science and Engineering

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