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![HTTP Adaptive Streaming (HAS)
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https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
● 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]
○ 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]
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2023. [Online]. Available: https://www.sandvine.com/global-internet-phenomena-report-2023](https://image.slidesharecdn.com/ieeeicc23sarena-230831063223-c9cf4633/85/IEEE_ICC-23_SARENA-pdf-6-320.jpg)
Uploaded byReza Farahani
IEEE_ICC'23_SARENA.pdf
The document proposes SARENA, an architecture that leverages SDN, SFC, and edge computing to efficiently deliver video streams with different QoE requirements. SARENA includes virtual proxy, cache, and transcoding functions distributed across the edge and infrastructure layers. An optimization model and heuristic solve for optimal service function chains and resource allocation to maximize QoE. An evaluation in a large-scale testbed showed SARENA improved users' QoE by 39.6%, latency by 29.3%, and network utilization by 30% compared to baseline approaches. Future work includes reinforcement learning methods and FaaS-enabled solutions.
IEEE_ICC'23_SARENA.pdf
- 1. SARENA: SFC-Enabled Architecturefor Adaptive Video Streaming Applications International Conference on Communications (ICC) May 29th , 2023 reza.farahani@aau.at | https://www.rezafarahani.me Reza Farahani, Abdelhak Bentaleb , Christian Timmerer, Mohammad Shojafar, Radu Prodan, and Hermann Hellwagner
- 2. Agenda ● Introduction ● ProposedSolution ○ SARENA Architecture ○ Optimization Model ○ Heuristic Approach ● Performance Evaluation ○ Setup ○ Methods/Metrics ○ Results ● Conclusion and Future Work
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- 6. HTTP Adaptive Streaming(HAS) 1 https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/ ● 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] ○ 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] [1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2023. [Online]. Available: https://www.sandvine.com/global-internet-phenomena-report-2023
- 7. Video Streaming Challenges 2 ●OTT video ● Live video streaming ● Immersive multimedia ● Video Gaming ● Video analytics for security, quality assurance, etc. Increase in amount of video generated and transported
- 8. Video Streaming Challenges 2 ●OTT video ● Live video streaming ● Immersive multimedia ● Video Gaming ● Video analytics for security, quality assurance, etc. Increase in amount of video generated and transported Versatile QoE, QoS requirements Resolution (4K, 8K) Latency (LL,ULL) Bitrate
- 9. Video Streaming Challenges 2 ●OTT video ● Live video streaming ● Immersive multimedia ● Video Gaming ● Video analytics for security, quality assurance, etc. Increase in amount of video generated and transported versatile QoE, QoS requirements Resolution (4K, 8K) Latency (LL,ULL) Bitrate
- 10. 3 Research Questions ✔ Howto leverage modern networking/computing paradigms to serve different MSs requests with acceptable QoE and improved network utilization? ✔ How to design a network-assisted HAS scheme without client-side modification ? ✔ How we can implement and evaluate proposed approach in a large-scale testbed? SDN S F C HAS E d g e
- 11. Content Delivery Network(CDN) 4
- 12. Edge Computing 5 The SPEC-RGReference Architecture for the Edge Continuum. Jansen, Matthijs, Auday Al-Dulaimy, Alessandro V. Papadopoulos, Animesh Trivedi, and Alexandru Iosup.
- 13. Service Function Chaining(SFC) 6 VNF i VNF i+1 VNF n VNF i VNF i+1 VNF n SFC Chains Chain 1 Chain m … … . . .
- 14. Service Function Chaining(SFC) 6 VNF i VNF i+1 VNF n VNF i VNF i+1 VNF n SFC Chains Chain 1 Chain m … … . . . Orchestration Placement Scheduling SFC Definition VNF Definition
- 15. ✔ Traditional networkarchitecture: ◆ Complex Network Devices ◆ Management Overhead ◆ Limited Scalability Software-Defined Networks (SDN) 7 Data Plane Control Plane
- 16. ✔ Conventional networkarchitecture: ◆ Complex Network Devices ◆ Management Overhead ◆ Limited Scalability ✔ The control plane (forwarding decision) is decoupled from the data plane (acts on the forwarding decision): ◆ Centralized Network Controller ◆ Standard communication Interface (OpenFlow) ◆ Programmable Open APIs 7 Source: https://opennetworking.org/sdn-definition/ Data Plane Control Plane Software-Defined Networks (SDN)
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- 18. SARENA Architecture 8 Virtual ProxyFunction Virtual Cache Function Virtual Transcoding Function 1 2 3 Multimedia VNFs
- 19. SARENA Architecture 8 Virtual ProxyFunction Virtual Cache Function Virtual Transcoding Function CDN Cache Origin Cache 1 2 3 4 5 Multimedia VNFs
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- 21. 9 ✔ The RequestsScheduler run an MILP optimization model to respond: ◆ Where is the optimal place for fetching the content quality level requested by each client, while efficiently employing layers’ available resources and satisfying service requirements (e.g., service deadlines)? ◆ How can we use the functions/services provided in the EL and IL layers to form MS function chains (SFCs)? ◆ What is the optimal SFC for responding to the requested quality level with specific service requirements? Optimization Model
- 22. Minimize total MSsserving times (i.e., fetching time plus transcoding time) ✔ chain Selection constraint ✔ Latency Calculation constraints ✔ Service Policy constraints ✔ Resource Utilization constraints 10 ✔ Constraints : ✔ Objective : Central Optimization Model
- 23. 11 ✔ The proposedMILP model is NP-hard and suffers from high time complexity ✔ Divide tasks between Edge and the SDN controller Heuristic Solution Virtual Scheduler Function Stats/Requests Collector (SRC) Requests Scheduler (RES) Interval
- 24. 12 Edge Server HeuristicAlgorithm
- 25. 13 SDN Controller HeuristicAlgorithm
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- 27. ✔ Large-scale cloud-basedtestbed, including 280 elements and real backbone topology ○ Xen virtual machines ○ 250 Dash player ○ Four Apache cache servers and an origin server ○ 19 backbone switches and 45 layer-2 links ○ Five edge server ○ Floodlight SDN controller ○ BOLA ABR algorithms ○ FFmpeg transcoders ○ LRU cache replacement policy ○ Zipf distribution is used for video and channel access popularity Evaluation Setup 14
- 28. Evaluation Setup 15 0.089 320 480 720 1080 1080 0.262 0.791 2.4 4.2 Resolution (p)Bitrate (Mbps) Bitrate (Mbps) Resolution (p) 20 VoDs, 300 sec. duration, 4 sec. segments 320 480 720 720 1080 1080 1080 0.128 0.320 0.780 1.4 2.4 3.3 3.9 5 live ch, 300 sec. duration, 2 sec. segments
- 29. ✔ Baseline systems: ◆CDN-assisted (CDA) ◆ Non VNF-assisted (NVA) ◆ Non VTF-enabled (NTE) ◆ Non Reconfiguration-enabled (NRE) ✔ 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 ◆ ASL: overall time for serving ◆ NCV: Network Cost Value ◆ ETR: Edge/P2P Transcoding Ratio ◆ BTL: Backhaul Traffic Load Evaluation Methods/Metrics 16
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- 33. ✔ Use thecooperation of SDN, SFC, and edge computing to serve efficiently various types of MSs with different QoE requirements. ✔ The experimental results over a large-scale testbed show: ○ users’ QoE by at least 39.6%, ○ latency by 29.3% ○ network utilization by 30%. ✔ Propose RL-based approaches and design FaaS-enabled solutions are our future directions. Conclusion and Future Work 19
- 34. Thank you foryour attention reza.farahani@aau.at | https://www.rezafarahani.me All rights reserved. ©2020 34