MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

1. Samira Afzal, Zahra Najafabadi Samani, Narges Mehran, Christian Timmerer, and Radu Prodan Institute of Information Technology (ITEC), Alpen-Adria-Universität Austria samira.afzal@aau.at | https://athena.itec.aau.at/ MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum APOLLO

2. Agenda Motivation and main objectives MPEC2 Evaluated results Conclusion 1

3. Motivation and Main Objectives Video streaming is a significant part of the current network traffic Computationally intensive Costly Time-consuming Video encoding is Minimizing cost and/or minimizing encoding time Making a trade-off between encoding time and cost Considering encoding time deadline and cost limitation To select Cloud/Fog resources for video encoding/transcoding operations, aiming at: 2

4. Motivation and Main Objectives Video streaming is a significant part of the current network traffic Computationally intensive Costly Time-consuming Video encoding is Minimizing cost and/or minimizing encoding time Making a trade-off between encoding time and cost Considering encoding time deadline and cost limitation To select Cloud/Fog resources for video encoding/transcoding operations, aiming at: 3

5. MPEC2 Multilayer and Pipeline Video Encoding on the Computing Continuum (MPEC2) 4

6. MPEC2 Architecture Overview Coordinator 5

11. 10 Objective Function Segment scheduling objective Stream scheduling objective

12. Objective Function Segment scheduling objective Stream scheduling objective Media service provider or user priorities 𝛼 ∈ [0, 1] Time-optimized (𝛼 = 1) Cost-optimized (𝛼 = 0) Encoding application Completion time Total cost Segment Instance Scene's segments 11

13. MPEC2 Architecture Overview Selects an instance type optimizing the objective O1 Distributes segments in a pipeline model on the instances optimizing the objective O2 Coordinator 12

14. Experimental Setup 13

15. Experimental Infrastructure Video Stream Characteristics 14

16. Evaluation Segment encoding scheduling Scene encoding scheduling Related work comparison 15

17. Segment Encoding Scheduling Time-optimized scenario Cost-optimized scenario 16

18. Time vs. Cost-optimized Scenarios Time-optimized results are 86 % faster and 77 % more expensive than the cost-optimized ones 17

19. Scene Encoding Scheduling Time-optimized scenario Cost-optimized scenario 18

20. Coordinater on c5a.2xlarge (Frankfurt) Time-optimized Scenario 19

21. Time-optimized Scenario Segment encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) 20

22. Time-optimized Scenario Segment encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) 21

23. Scene encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) Time-optimized Scenario 5 number of c5.9xlarge 10 number of c5.4xlarge 5 number of r5.4xlarge Total completion time 22

24. Cost-optimized Scenario Segment encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) 10 number of medium Scene encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) Total completion time 23

25. Related Work Comparison MAPO: is a multi-objective model for IoT application placement in a Fog environment selecting one instance type for encoding the video stream Random: selects one arbitrary instance type per scene to encode the video stream Fastest: selects the fastest instance type relying on the CPU speed to encode the video stream Cheapest: selects the lowest cost instance type to encode the video stream Narges Mehran, Dragi Kimovski, and Radu Prodan. MAPO: a multi-objective model for iot application placement in a fog environment. In Proceedings of the 9th International Conference on the Internet of Things, pages 1–8, 2019 1 1 24

26. Related Work Comparison 25

27. Conclusions Proposed MPEC2 for video encoding application on the computing continuum MPEC2 method: Scene detection Segment encoding scheduling, utilizing a multilayer graph partitioning model Scene encoding scheduling, proposing a pipeline model Highlighted factors: User or media service priorities, such as cost and/or time priorities Resources priorities, location, type, cost, number of instances for each resource type Segment properties, such as duration, number of segments, segment encoding time Evaluated MPEC2 on a set of Cloud AWS and Fog Exoscale resource instances Achieved significant improvements: 24%, 54%, and 40% faster video encoding compared to MAPO, random, and fastest instance selection methods in time-optimized scenarios 60 %, 50%, and 5% cheaper compared to the MAPO, random, and cheapest methods in time-optimized scenarios 26

28. Thank you Institute of Information Technology (ITEC) Alpen-Adria-Universität Austria samira.afzal@aau.at https://itec.aau.at/ Have a great day ahead! APOLLO

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