IEEE GLOBECOM 2022, Online Learning RICHTER

1. Hybrid P2P-CDN Architecture for Live Video Streaming:
An Online Learning Approach
IEEE Global Communication Conference (GLOBECOM 2022)
December 5th
, 2022
reza.farahani@aau.at | https://athena.itec.aau.at/ | https://www.rezafarahani.me
Reza Farahani, Abdelhak Bentaleb , Ekrem. Cetinkaya, Christian Timmerer, Roger Zimmermann, and Hermann Hellwagner

2. Agenda
● Introduction
● Motivation
● Proposed Solution
○ System Architecture
○ Optimization Model
○ Online Learning Approach
● Performance Evaluation
○ Setup
○ Methods/Metrics
○ Results
● Conclusion and Future Work

3. Introduction
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4. ● Video streaming traffic has become the primary type of traffic over the Internet.
○ It includes 53.72% of the total video traffic over the Internet [1]
○ HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of
video streams [1]
○ Live video streaming has become significantly popular, i.e., 17% of the total video traffic by 2022 [1]
Introduction- Video Streaming
4
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/

5. Introduction- Video Delivery (CDN)
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✔ CDNs scale HAS delivery systems
✔ Growth in high-quality and low latency live
video demands
◆ Overload CDN servers
◆ OTT services fail to deliver a satisfactory
quality and latency to end-users

6. Introduction- Video Delivery (P2P)
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✔ Alleviate network congestion
✔ Increase streaming stability
✔ Reduce delivery costs
✔ Scalability issue
Tracker
Peers

7. Motivation
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8. Motivation
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✔ Design a hybrid P2P-CDN live streaming system
◆ Employ both computational and bandwidth capabilities provided by the P2P network
◆ Utilize P2P and CDN resources efficiently through modern networking paradigms
◆ Satisfy HAS client requests with high QoE and low latency
CDN P2P

9. Proposed Solution
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10. Proposed Solution- System Architecture
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✔ We leverage the NFV, and edge computing technologies and proposes
◆ RICHTER as hybRId P2P-CDN arcHiTecture for livE video stReaming
✔ RICHTER employs smart VTSs at the edge of a hybrid system
✔ RICHTER uses storage, computational and bandwidth resources provided by VTS, P2P and CDNs

11. Proposed Solution- System Architecture (cont)
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✔ we leverage the NFV, and edge computing technologies and proposes
◆ RICHTER as hybRId P2P-CDN arcHiTecture for livE video stReaming
✔ RICHTER employs smart VTSs at the edge of a hybrid system
✔ RICHTER uses storage, computational and bandwidth resources provided by VTS, P2P and CDNs

12. 12
✔ VTS servers run an MILP optimization model to respond to the following key questions:
1. Where is the optimal place (i.e., adjacent peers, VTS, CDN servers, or origin server) in terms of the
lowest latency for fetching each client’s requested content quality level from, while efficiently
utilizing the available resources?
2. What is the optimal approach for responding to the requested quality level (i.e., fetch or transcode)?
Proposed Solution- Optimization Model

13. Minimize total Peer serving times (i.e., fetching time plus transcoding time)
✔ Action Selection (AS) constraint
✔ Serving Time (ST) constraints
✔ CDN/Origin/Peer (CP) constraints
✔ Resource Usage (RS) constraints
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✔ Constraints :
✔ Objective :
Proposed Solution- Optimization Model

14. 14
✔ The proposed MILP model is NP-hard and suffers from high time complexity
✔ Leverage new modules, classification technique to introduce an OL heuristic approach
✔ Self Organizing Map (SOM) is adopted as the request management solution
in the OL agent:
◆ popular technique for unsupervised classification problems
◆ can be applied to solve NP-hard problems
◆ does not require a prepared dataset for supervised model training
◆ allows online real-time decision-making
◆ evolves its model quickly over time
Proposed Solution- Online Learning (OL) Approach

15. 15
Proposed Solution- Online Learning (OL) Approach

16. 16
Proposed Solution- Online Learning (OL) Approach

17. 17
Proposed Solution- Online Learning (OL) Approach
Node
Action
Req#
Violation

18. Performance Evaluation
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19. ✔ Large-scale cloud-based testbed, including 375 elements and real backbone topology:
○ Xen virtual machines
○ 350 clients
○ Four cache servers and an origin server
○ 19 backbone switches and 45 layer-2 links
○ A VTS server
○ Five Video Channel (CHI -- CH V)
■ 300s video sequence
■ 2 seconds segments
■ five representations (0.089, 0.262, 0.791, 2.4, 4.2 Mbps)
○ BOLA ABR algorithms
○ FFmpeg transcoders over P2P and VTS
○ LRU cache replacement policy
○ Zipf distribution is used for channel access popularity
○ Apple M1, Xiaomi Mi11, and iPhone 11
Performance Evaluation- Setup
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20. ✔ Baseline systems:
◆ Non Hybrid (NOH)
◆ Non Transcoding-enabled Hybrid (NTH)
◆ Edge Caching/Transcoding Hybrid (ECT)
✔ The performance of the aforementioned approaches is evaluated through
◆ ASB: Average Segment Bitrate
◆ AQS: Average Number of Quality Switches
◆ ANS: Average Number of Stalls
◆ ASD: Average Stall Duration
◆ APQ: Average Perceived QoE calculated by ITU-T P.1203 mode 0
◆ AST: overall time for serving all clients including fetching time plus transcoding
◆ CHR: Cache Hit Ratio
◆ ETR: Edge/P2P Transcoding Ratio
◆ BTL: Backhaul Traffic Load
Performance Evaluation- Methods/Metrics
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21. ✔ Running transcoding on peers must:
○ be fast enough
○ not significantly impose a delay to the live system
○ not consume much battery
✔ Playout : 0.8% Transcode: 0.4% Playout+Transcode+Transmit : 1.3%
Performance Evaluation- Results
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254.2 /150 = 1.69 sec

22. Performance Evaluation- Results
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◆ ASB: Average Segment Bitrate
◆ AQS: Average Number of Quality Switches

23. Performance Evaluation- Results
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◆ ANS: Average Number of Stalls
◆ ASD: Average Stall Duration

24. Performance Evaluation- Results
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◆ APQ: Average Perceived QoE calculated by ITU-T P.1203 mode 0
◆ AST: overall time for serving all clients including fetching time plus transcoding

25. Performance Evaluation- Results
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◆ CHR: Cache Hit Ratio
◆ ETR: Edge/P2P Transcoding Ratio
◆ BTL: Backhaul Traffic Load

26. Conclusion and Future Work
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27. ● This paper leverages the NFV, Edge computing, OL paradigms to propose the RICHTER
framework as a Hybrid P2P-CDN system for live video streaming applications
● We design architecture and formulate the problem as an optimization model
● We propose a OL heuristic approach works in practical scenarios
● We implement the proposed framework on a large-scale testbed consisting of 350
peers and conducts experiments for measuring QoE and Network Utilization metrics
● RICHTER outperforms baseline schemes in terms of users’ QoE, latency and the
network utilization by at least 59%, 39% and 70%, respectively
● Extending proposed Action tree is possible future work directions.
Conclusion and Future Work
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28. Thank you for your attention
reza.farahani@aau.at | https://www.rezafarahani.me | https://athena.itec.aau.at/
All rights reserved. ©2020
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