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MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

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MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

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Video streaming is the dominating traffic in today’s data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost.
In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances.
We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%-50% lower completion time and 5-60% reduced total cost.

Video streaming is the dominating traffic in today’s data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost.
In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances.
We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%-50% lower completion time and 5-60% reduced total cost.

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MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

  1. 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. 2. Agenda Motivation and main objectives MPEC2 Evaluated results Conclusion 1
  3. 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. 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. 5. MPEC2 Multilayer and Pipeline Video Encoding on the Computing Continuum (MPEC2) 4
  6. 6. MPEC2 Architecture Overview Coordinator 5
  7. 7. MPEC2 Architecture Overview Coordinator 6
  8. 8. MPEC2 Architecture Overview Coordinator 7
  9. 9. MPEC2 Architecture Overview Coordinator 8
  10. 10. MPEC2 Architecture Overview Coordinator 9
  11. 11. 10 Objective Function Segment scheduling objective Stream scheduling objective
  12. 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. 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. 14. Experimental Setup 13
  15. 15. Experimental Infrastructure Video Stream Characteristics 14
  16. 16. Evaluation Segment encoding scheduling Scene encoding scheduling Related work comparison 15
  17. 17. Segment Encoding Scheduling Time-optimized scenario Cost-optimized scenario 16
  18. 18. Time vs. Cost-optimized Scenarios Time-optimized results are 86 % faster and 77 % more expensive than the cost-optimized ones 17
  19. 19. Scene Encoding Scheduling Time-optimized scenario Cost-optimized scenario 18
  20. 20. Coordinater on c5a.2xlarge (Frankfurt) Time-optimized Scenario 19
  21. 21. Time-optimized Scenario Segment encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) 20
  22. 22. Time-optimized Scenario Segment encoding scheduling Coordinater on c5a.2xlarge (Frankfurt) 21
  23. 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. 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. 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. 26. Related Work Comparison 25
  27. 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. 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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