Scadoosh: Scaling Down the Footprint of Rate-Adaptive Live Streaming on CDN Infrastructure
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The document discusses the challenges and objectives in scaling CDN infrastructure to meet the increasing demand for real-time video streaming, projecting a need for a hundredfold expansion in the coming years. It introduces a framework called 'Scadoosh' aimed at optimizing content delivery by intelligently managing how video representations are distributed among edge servers, thereby enhancing quality of experience while potentially reducing infrastructure needs. The conclusions highlight the critical role of adaptive streaming technology in shaping CDN strategies and the ongoing development of Scadoosh to realize these goals.
Scadoosh: Scaling Down the Footprint of Rate-Adaptive Live Streaming on CDN Infrastructure
- 2. Motivations Akamai’s infrastructure will have to expand by a factor of 100 times in the next five years just to keep up with the demand for real-time video. Paul Sagan, Akamai CEO, Jun 2012 2 / 15 Gwendal Simon Scadoosh
- 3. Live stream delivery Content Provider CDN edge encoders Clients servers ingest origin server server 3 / 15 Gwendal Simon Scadoosh
- 4. Live stream delivery Content Provider CDN edge encoders Clients servers ingest origin server server disclaimer : ISP strategy is out of the scope of this talk 3 / 15 Gwendal Simon Scadoosh
- 5. Rate-adaptive video streaming HTTP streaming server client video segments requests 4 / 15 Gwendal Simon Scadoosh
- 6. Rate-adaptive video streaming HTTP streaming server client video segments requests representation 3 representation 2 representation 1 time 4 / 15 Gwendal Simon Scadoosh
- 7. Rate-adaptive video streaming HTTP streaming server client video segments requests bit-rate representation 3 available bandwidth representation 2 representation 1 time time 4 / 15 Gwendal Simon Scadoosh
- 8. Rate-adaptive video streaming A1 50 kbps 320 x 240 D1 900 kbps 1280 x 720 A2 100 kbps 320 x 240 D2 1.2 Mbps 1280 x 720 A3 150 kbps 320 x 240 D3 1.5 Mbps 1280 x 720 B1 200 kbps 480 x 360 D4 2.0 Mbps 1280 x 720 B2 250 kbps 480 x 360 E1 2.5 Mbps 1920 x 1080 B3 300 kbps 480 x 360 E2 3.0 Mbps 1920 x 1080 B4 400 kbps 480 x 360 E3 4.0 Mbps 1920 x 1080 B5 500 kbps 480 x 360 E4 5.0 Mbps 1920 x 1080 C1 600 kbps 854 x 480 E5 6.0 Mbps 1920 x 1080 C2 700 kbps 854 x 480 E6 8.0 Mbps 1920 x 1080 5 / 15 Gwendal Simon Scadoosh
- 9. Rate-adaptive video streaming A1 50 kbps 320 x 240 D1 900 kbps 1280 x 720 A2 100 kbps 320 x 240 D2 1.2 Mbps 1280 x 720 A3 150 kbps 320 x 240 D3 1.5 Mbps 1280 x 720 B1 200 kbps 480 x 360 D4 2.0 Mbps 1280 x 720 B2 250 kbps 480 x 360 E1 2.5 Mbps 1920 x 1080 B3 300 kbps 480 x 360 E2 3.0 Mbps 1920 x 1080 B4 400 kbps 480 x 360 E3 4.0 Mbps 1920 x 1080 B5 500 kbps 480 x 360 E4 5.0 Mbps 1920 x 1080 C1 600 kbps 854 x 480 E5 6.0 Mbps 1920 x 1080 C2 700 kbps 854 x 480 E6 8.0 Mbps 1920 x 1080 one video stream = a dozen of representations = more than 20 Mbps 5 / 15 Gwendal Simon Scadoosh
- 10. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors edge servers ISP 1 ISP 2 ISP 3 6 / 15 Gwendal Simon Scadoosh
- 11. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors edge servers last-mile ? ISP 1 ISP 2 ISP 3 6 / 15 Gwendal Simon Scadoosh
- 12. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors edge servers last-mile ? ISP 1 ISP 2 ISP 3 adaptive streaming → last-mile 6 / 15 Gwendal Simon Scadoosh
- 13. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors peering link ? edge servers ISP 1 ISP 2 ISP 3 adaptive streaming → last-mile 6 / 15 Gwendal Simon Scadoosh
- 14. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors peering link ? edge servers ISP 1 ISP 2 ISP 3 adaptive streaming → last-mile edge servers in ISP → peering link 6 / 15 Gwendal Simon Scadoosh
- 15. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors in edge-servers ? edge servers ISP 1 ISP 2 ISP 3 adaptive streaming → last-mile edge servers in ISP → peering link 6 / 15 Gwendal Simon Scadoosh
- 16. Identifying the issue Where is the bottleneck in today’s CDN ? origin servers reflectors in edge-servers ? edge servers ISP 1 ISP 2 ISP 3 adaptive streaming → last-mile edge servers in ISP → peering link commoditized hardware→ server under-provisioning 6 / 15 Gwendal Simon Scadoosh
- 17. Our objective Finding a trade-off between : infrastructure capacity for CDN providers quality of experience for clients 7 / 15 Gwendal Simon Scadoosh
- 18. Our objective Finding a trade-off between : infrastructure capacity for CDN providers quality of experience for clients Typical services : multiple simultaneous live streams user-generated live video services multiview live events second-screen services 7 / 15 Gwendal Simon Scadoosh
- 19. Scadoosh 8 / 15 Gwendal Simon Scadoosh
- 20. Main idea in a nutshell To not send all representations to all edge servers 9 / 15 Gwendal Simon Scadoosh
- 21. Formal Scadoosh e uij = utility score of representation i of channel j for edge server e 10 / 15 Gwendal Simon Scadoosh
- 22. Formal Scadoosh e uij = utility score of representation i of channel j for edge server e e 1 if repr i of chn j is delivered to e xij = 0 otherwise 10 / 15 Gwendal Simon Scadoosh
- 23. Formal Scadoosh e uij = utility score of representation i of channel j for edge server e e 1 if repr i of chn j is delivered to e xij = 0 otherwise objective : e e max uij × xij e j i 10 / 15 Gwendal Simon Scadoosh
- 24. Scadoosh Principle Scadoosh coordinator 11 / 15 Gwendal Simon Scadoosh
- 25. Scadoosh Principle 1 edge servers Scadoosh report to coordinator Scadoosh coordinator about their activities during last period 1 11 / 15 Gwendal Simon Scadoosh
- 26. Scadoosh Principle 2 2 Scadoosh coordinator Scadoosh coordinator decides utility scores, computes delivery forest overlay and notifies sources 11 / 15 Gwendal Simon Scadoosh
- 27. Scadoosh Principle 3 3 sources use the Scadoosh coordinator forest overlays to deliver the live representations to the edge servers 11 / 15 Gwendal Simon Scadoosh
- 28. What we have done Formulate an optimization problem Pascal Frossard (EPFL) Hervé Kerivin (Clemson then Clermont-Ferrand) 12 / 15 Gwendal Simon Scadoosh
- 29. What we have done Formulate an optimization problem Pascal Frossard (EPFL) Hervé Kerivin (Clemson then Clermont-Ferrand) Design optimal and approximate algorithms Jimmy Leblet (Lyon) and Fen Zhou (Avignon) Hanan Shpungin (University of Waterloo) 12 / 15 Gwendal Simon Scadoosh
- 30. What we have done Formulate an optimization problem Pascal Frossard (EPFL) Hervé Kerivin (Clemson then Clermont-Ferrand) Design optimal and approximate algorithms Jimmy Leblet (Lyon) and Fen Zhou (Avignon) Hanan Shpungin (University of Waterloo) Design and test a system Catherine Rosenberg (University of Waterloo) Jiayi Liu (Telecom Bretagne) 12 / 15 Gwendal Simon Scadoosh
- 31. Results Overview ratio of received encodings 1 1 0.8 0.8 0.6 mobile 0.6 xdsl 0.4 0.4 0.2 0.2 0 0 120%100% 80% 60% 40% 20% 120%100% 80% 60% 40% 20% provisioning provisioning 1 0.8 lowest representation 0.6 ftth low representation 0.4 normal representation 0.2 high representation highest representation 0 120%100% 80% 60% 40% 20% 13 / 15 Gwendal Simon Scadoosh
- 32. Conclusion 14 / 15 Gwendal Simon Scadoosh
- 33. Conclusion Rate-adaptive video streaming : widely adopted in video services threatening CDN infrastructure 15 / 15 Gwendal Simon Scadoosh
- 34. Conclusion Rate-adaptive video streaming : widely adopted in video services threatening CDN infrastructure Scadoosh is in preliminary stage : reduce infrastructure by a factor of 5 maintain quality of experience 15 / 15 Gwendal Simon Scadoosh
- 35. Conclusion Rate-adaptive video streaming : widely adopted in video services threatening CDN infrastructure Scadoosh is in preliminary stage : reduce infrastructure by a factor of 5 maintain quality of experience Stay tuned : http ://perso.telecom-bretagne.eu/gwendalsimon 15 / 15 Gwendal Simon Scadoosh