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Optimizing Cost and Performance for Content Multihoming

Optimizing Cost and Performance for Content Multihoming. SIGCOMM’12 -Piggy, 2013.03.18. Outline. What is Content Multihoming Goal Control Framework Global Optimization Local Adaptation Evalution. Content Multihoming. CDN Diversity. CDN DIVERSITY. CDN DIVERSITY. Goal.

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Optimizing Cost and Performance for Content Multihoming

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  1. Optimizing Cost and Performance for Content Multihoming SIGCOMM’12 -Piggy, 2013.03.18

  2. Outline • What is Content Multihoming • Goal • Control Framework • Global Optimization • Local Adaptation • Evalution

  3. Content Multihoming

  4. CDN Diversity

  5. CDN DIVERSITY

  6. CDN DIVERSITY

  7. Goal • Algorithms and protocols that optimize • Content publisher cost • Content viewer performance • A content object can be delivered from multiple CDNs, which CDN(s) should a content viewer use?

  8. Notation

  9. Control Framework

  10. Passive vs. Active Client • Passive client • Use one CDN edge server at a time • Active client • Adaptation algorithm • Multiple CDN servers for a single content object

  11. Problem Statement (Q) • QoEguarantee • CDN k is providing the required features to deliver content object i • exceeds the performance target • Cost optimization • Balance load to multiple CDNs to minimize total cost

  12. Active Client • Virtual CDN • Primary CDN • Backup CDN • k’ = (k, j)

  13. Computing Optimization(CMO) • Problem Q has an optimal solution which assigns a location object into a single CDN • K|A|N

  14. Basic Idea

  15. Extension • CDN subscription levels • Fix fee to different usage levels • Different levels as an individual CDN • Per-request cost • Extend vector dimension to R+1 • Multiple streaming rates • Independent content objects

  16. Local Adaptation • QoE protection • Prioritized guidance • Low session overhead

  17. Local Adaptation • Similar to TCP AIMD • Total workload control • Priority assignment

  18. Evaluation Setting

  19. Cost Saving

  20. Cost Saving

  21. Active Client Setting • Clients • 500+ Planetlab nodes with Firefox 8.0 + Adobe Flash 10.1 • Two CDNs • Amazon CloudFront • CDN3

  22. Active Client Test Case

  23. Stress Tests (Step-down)

  24. Stress Tests (Ramp-down)

  25. Stress Tests (Oscillation)

  26. Active Client QoE Gain

  27. Conclusion • We develop and implement a two-level approach to optimize cost and performance for content multihoming: • CMO: an efficient algorithm to minimize publisher cost and satisfy statistical performance constraints • Active client: an online QoE protection algorithm to follow CMO guidance and locally handle network congestions or server overloading

  28. Q&A

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