Analysis of Adaptive Streaming for Hybrid CDN/P2P Live Video Systems. A seminar presented by tangkai

1. ANALYSIS OF ADAPTIVE STREAMING
FOR HYBRID CDN/P2P LIVE VIDEO
SYSTEMS
Ahmed Mansy and Mostafa Ammar
School of CS, GIT
Presented by Tangkai

2. ABOUT THE AUTHOR
 Ahmed Mansy
 PhD Student
 scalable adaptive video streaming over the Internet.
 message ferry routing in Disruption Tolerant
Networks (DTNs).
 Mostafa Ammar
 Regents’ Professor & Associate Chair
 General Interest: Computer Network Architectures
and Protocols.
 Current Specific Interests: Overlay
Networks, Network Virtualization, Mobile Wirless
Networks, Disruption Tolerant Networks.

3. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

4. INTRODUCTION
 Video ~ dominate traffic of the Internet.
 33% in 2010 ~ 57% in 2014 (expected)
 Streaming stored or live video exclude P2P sharing
 CDN ~ pillar of the video distribution
 Aim: delay and throughput
 CDN -> edge server
 CDN + adaptive streaming => DASH

5. INTRODUCTION
 P2P streaming
 280 PT/month in 2009
 P2P + adaptive streaming => layered streaming
 Cons:
 Complicated (design)
 High processing power (client)
 Not attractive for commercial use
 Pros:
 Cost-efficiency
 CDN/P2P Hybrid System

6. RELATED WORK
 Previous works[8][11] on designing such system
 LiveSky: operational commercial sys
 10m users
 1st work study adaptive streaming in CDN/P2P hybrid sys
[8] C. Huang, J. Li, , and K. Ross, “Can internet video-on-demand be profitable?” in Sigcomm, 2007.
[11] Hao Yin and Xuening Liu and Tongyu Zhan and Vyas Sekar and Feng Qiu and Chuan Lin and Hui Zhang
and and Bo Li, “Design and Deployment of a Hybrid CDN-P2P System for Live Video Streaming: Experience
with LiveSky,” in Multimedia, 2009.

7. IDENTIFY THE PROBLEM
 Assumption
 Static in client: no switch/wired ap/constant bw
 Dynamic in process: departure and arrival
 Bitrate adaption strategy
 Linear optimization problem to obtain best suitable bitrate
 CDN/P2P mode switch rule
 Stochastic fluid model to obtain lower bound of num of user
 Interaction between two decision and how they affect each
other

8. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

9. DNS REDIRECTION
 Typical
DNS
Lookup
 4. Root
DNS
Server
 9.
Perfor
ming
cache

10. SYSTEM DESCRIPTION
Core server
DNS Redirection Edge server

11. DNS REDIRECTION
 [16]
[16] A.-J. Su, D. Choffnes, A. Kuzmanovic, and F. Bustamante, “Drafting behind akamai,” in
SIGCOMM, 2006.

12. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

13. SINGLE RATE SYSTEM MODEL
 Definition
 Seeder/leecher
 Directly connected to CDN
 Unconstrained/constrained
 Unlimited number of connections to other peers
 Churnless/churn
 Fixed number of client
 Assumption
 Upload rate of all seeder or leecher are the same
(l ) (s)
ui ul uj us

14. SINGLE RATE SYSTEM MODEL
 Unconstrained churnless system
 To support r, at least ns seeder
nsu s nl u l nl r ul
r ns
nl nl

15. SINGLE RATE SYSTEM MODEL
 Unconstrained churn system
 Assumption:
 User arrival follows Poisson process with rate λ[19]
 User stay in sys for a period of time follows general probability
distribution with mean 1/γ
 Churn happens in leech node only
 Total number of user in system N(t) ~ Poisson distribution
with rate ρ= λ/γ
 Simple admission policy
 [19] K. Sripanidkulchai, B. Maggs, and H. Zhang, “An analysis of live streaming workloads on the
internet,” in Internet Measurement Conference (IMC), 2004.

16. SINGLE RATE SYSTEM MODEL
 Formulation
 Poisson distribution(large ρ) -> Gaussian distribution
Low bound

17. SINGLE RATE SYSTEM MODEL
 Constrained churnless system
 Def
 Sin number of incoming connection a seeder can accept.(s<-l)
 Yin number of incoming connection a leecher can accept.(l<-l)
 Yout number of connection leecher can initiate. (l->l+s)
 η as the efficiency of the P2P protocol.
 Probability leecher can find new content in other leechers.
 d as the average download rate for any leecher

18. SINGLE RATE SYSTEM MODEL
 = average num of seeder
connected to each leecher.

 Average leecher download rate is not directly related to the
constraints of the system Sin/Yin.
 only difference is η with unconstrained churnless sys.

19. SINGLE RATE SYSTEM MODEL
 Constrained system with churn
 Estimation -> bound
 N ~ Gaussian dist( ), (1 − α) confidence interval
,

20. SINGLE RATE SYSTEM MODEL


 is inversely proportional to ρ which means that the higher
client arrival rates λ and the longer clients stay in the system
1/γ, the lower becomes.
 High guarantee of number of seeder

21. SINGLE RATE SYSTEM MODEL


22. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

23. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 Problem
 Which clients should be downgraded to streams of lower
bitrates?
 What should these new lower bitrates be?
 How to get an optimal allocation of bitrates to clients while
minimizing client downgrading?
 Does the adaptive solution always exist?
 Object
 client dissatisfaction: difference between bitrate it requested
and it actually received
 Minimize total client dissatisfaction over all clients.

24. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 Unconstrained churnless system
 Def:
 Bitrates provided by the CDN r1 > r2 > ... > rR
 Define xij as the fraction of clients that request bitrate ri but receive
bitrate rj

25. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 Linear Optimization problem has a solution. values for xij
and ns i
 ns the number of seeders that should receive video of bitrate ri
i
from the proxy.
 ns =0
i
 bitrate ri will not be supported by the server
 no clients requested bitrate ri
 some clients requested ri but the server decided not to deliver it and
downgraded these clients to lower bitrates
 ns >0
i
 does not necessarily mean some clients requested bitrate ri
 it could mean that no clients requested rate ri but the server chose to
downgrade some of the clients
 xij randomly choose fraction of leecher requested ri and delivered rj

26. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 Unconstrained churn system
 client will request a video stream of bitrate with probability
 where λ is the general client arrival rate
 number of clients of bitrate at any time in the system
becomes a Poisson random variable with an average
 Non-linear optimization problem. Use a linear approximation

27. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 Constrained churnless system
 Constrained churn system

28. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 CDN adaptive live streaming
Ce r 1
 guarantees with confidence (1 − α) that edge server capacity will
be sufficient for providing bitrate r to arriving clients with rate ρ.

29. ADAPTIVE HYBRID LIVE VIDEO STREAMING
 CDN v.s. Hybrid Performance
 Churnless
 Linear optimzation problem -> xij
 Churn
 approximation

30. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

31. ANALYSIS VALIDATION
 Validate single bitrate streaming only
 On BitTorrent
 Tracker: proxy
 Seeder: download torrent and video files
 Leecher: download torrent
 Parameter
 10s chuck
 Us/Ul 350kbps/500kbps
 ρ 100~400 clients/hour
 γ ~ mixed-exponential distribution PDF
 Sin = 20, Yin = 10

32. ANALYSIS VALIDATION
 Solid line means enough seeder to support bitrate
 Fig 4(a) – Fig2(b)

33. ANALYSIS VALIDATION

34. OUTLINE
 Introduction
 System Description
 Single Rate System Model
 Adaptive Hybrid Live Video Streaming
 Analysis Validation
 Illustrative Case Study

35. ILLUSTRATIVE CASE STUDY
 Metric
 Inter-client fairness
 Request and actually received
 Saving in CDN server capacity
 Profile
 low/uniform/high (for bitrate)

36. ILLUSTRATIVE CASE STUDY
 Inter-client fairness
 Single bitrate manner
 Downgrade for all if overloaded.
 Adaptive: fairness drop
 Single bitrate
 Start at lower than 100%/Constant/even better

37. ILLUSTRATIVE CASE STUDY
 Capacity saving
 Fairness->100%
 Saving is less in high profile: asymmetric bw(US/China)

38. Thank you!