My talk in IEEE International Conference on Communication (IEEE ICC 2023)

1. SARENA: SFC-Enabled Architecture for 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
● Proposed Solution
○ SARENA Architecture
○ Optimization Model
○ Heuristic Approach
● Performance Evaluation
○ Setup
○ Methods/Metrics
○ Results
● Conclusion and Future Work

3. Introduction

4. Motivation

5. Proposed Solution

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
✔ How to 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-RG Reference 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 network architecture:
◆ Complex Network Devices
◆ Management Overhead
◆ Limited Scalability
Software-Defined Networks (SDN)
7
Data Plane
Control Plane

16. ✔ Conventional network architecture:
◆ 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)

17. SARENA Architecture
8

18. SARENA Architecture
8
Virtual Proxy Function
Virtual Cache Function
Virtual Transcoding Function
1
2
3
Multimedia
VNFs

19. SARENA Architecture
8
Virtual Proxy Function
Virtual Cache Function
Virtual Transcoding Function
CDN Cache
Origin Cache
1
2
3
4
5
Multimedia
VNFs

20. 3
SARENA Architecture
8
1
2
5
Multimedia
SFCs
1
2
4
1
1
4
1 3

21. 9
✔ The Requests Scheduler 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 MSs serving 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 proposed MILP 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 Heuristic Algorithm

25. 13
SDN Controller Heuristic Algorithm

26. Performance Evaluation

27. ✔ Large-scale cloud-based testbed, 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

30. Evaluation Results
17

31. Evaluation Results
18

32. Conclusion and Future Work

33. ✔ Use the cooperation 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 for your attention
reza.farahani@aau.at | https://www.rezafarahani.me
All rights reserved. ©2020
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