The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today’s internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.

1. Samira Afzal, Narges Mehran, Sandro Linder, Christian Timmerer, and Radu Prodan
Institute of Information Technology (ITEC), Alpen-Adria-Universität Austria
samira.afzal@aau.at | https://athena.itec.aau.at/
VE-Match: Video Encoding Matching-based Model
for Cloud and Edge Computing Instances

2. 1
Agenda
Motivation and main
objectives
VE-Match
Experimental results
Conclusion

3. Video streaming accounts for 67.60%
current network traffic
2
https://www.bbc.com/future/article/20200305-why-your-internet-habits-are-not-as-clean-as-you-think
1
2
2
https://www.sandvine.com/hubfs/Sandvine_Redesign_2019/Downloads/2023/reports/Sandvine%20GIPR%202023.pdf
Urgent action is needed against climate change and global greenhouse gas
(GHG) emissions
The carbon footprint of Internet data traffic accounts for about 3.7% of
GHG
Motivation
1

4. 2 3
Main Objectives
Computationally intensive
Costly
Time-consuming
Energy intensive
Video encoding is
Minimizing the aggregate requirements of the media and resource
providers
Minimizing cost or minimizing energy consumption
Making a trade-off between energy usage and cost
To select Cloud/Edge Instances for video encoding/transcoding operations,
aiming at:

5. VE-Match

6. 4

7. 5
VE-Match
Video encoding application consisting of codec, bitrate, and
resolution set for encoding a video segment
VE-Match, a matching-based method to schedule video encoding
applications on both Cloud and Edge resources to optimize costs
and energy consumption

8. Video Encoding
Matching-based Model
for Cloud and Edge
Computing Instances
(VE-Match)
VE-Match
6

9. 7
VE-Match Architecture Overview

10. 8
Objective Function
To match each encoding application A to an instance I that minimizes 𝑂 (A, I)
based on two independent goals of cost C and energy E on the players’ sides
where 0 < 𝛼 + 𝛽 ≤ 2 define the competition between the
media (defined by 𝛼 on the application side) and
resource (defined by 𝛽 on the instance side) providers’

11. Evaluation
Application-side cost-
optimized
Instance-side energy-
optimized
Tradeoff
9

12. 10
Experimental Infrastructure
Germany Austria
Cheapest with
highest energy cons.
Most expensive with
Lowest energy cons.

13. 11
Encoding Energy Benchmark
Benchmark
four types of Cloud AWS EC2
instances and one type of Edge
server
500 video encoding applications
4K video resolutions, HEVC
format
The cost of the medium instance is
higher than that of all AWS Cloud
instances
The energy consumption of the
medium instance is lower than all
AWS Cloud

14. 12
VE-Match Experimental Results
77.85% cost reduction
45.42% energy reduction

15. 13
VE-Match CO2 Emission Analysis
80% CO2 emission reduction
Source materials in power production

16. Conclusions
The proposed VE-Match
employs game-theoretic principles
aims at minimizing video encoding cost and/or energy by selecting the
appropriate instance types of the Cloud and Edge
We evaluated VE-Match in a real computing testbed
varying the number of video applications
across Cloud and Edge Instances
Our experimental results demonstrate that
Video encoding cost reduction by 17%-78% in the cost-optimized scenarios
Video encoding energy consumption reduction by 38%-45% in the energy-
optimized scenarios along with 80% CO2 emission reduction
14

17. Thank you
Have a
great day
ahead!
Paper link: https://dl.acm.org/doi/10.1145/3593908.3593943
Institute of Information Technology (ITEC) Alpen-Adria-Universität Austria
samira.afzal@aau.at https://itec.aau.at/