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.

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
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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

7. MPEC2 Architecture Overview
Coordinator
6

8. MPEC2 Architecture Overview
Coordinator
7

9. MPEC2 Architecture Overview
Coordinator
8

10. MPEC2 Architecture Overview
Coordinator
9

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
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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
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19. Scene
Encoding
Scheduling
Time-optimized scenario
Cost-optimized scenario
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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
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1
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26. Related Work Comparison
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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
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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!
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