VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing Instances
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The document presents VE-Match, a video encoding matching model aimed at optimizing costs and energy consumption in cloud and edge computing for video encoding applications. By employing game-theoretic principles, VE-Match achieved significant reductions, including 77.85% in cost and 45.42% in energy, while also reducing CO2 emissions by 80%. Experimental results validate the model's effectiveness in varying operational scenarios.
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing Instances
- 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
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- 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
- 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’
- 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/