The document presents OTEC, an optimized transcoding task scheduler for cloud and fog environments aimed at improving video encoding efficiency by balancing cost and time. It utilizes a mixed integer linear programming model to allocate resources based on priorities, demonstrating that time-optimized scenarios are 30.5% faster but 24.78% more expensive than cost-optimized ones. The findings emphasize the trade-offs in transcoding processes influenced by administrator and resource priorities.

1. Samira Afzal, Farzad Tashtarian, Hamid Hadian, Alireza Erfanian, Christian Timmerer, and Radu Prodan
Institute of Information Technology (ITEC), Alpen-Adria-Universität Austria
samira.afzal@aau.at | https://athena.itec.aau.at/
OTEC: An Optimized Transcoding Task Scheduler for
Cloud and Fog Environments
APOLLO

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

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 limit
To select Cloud/Fog resources for video encoding/transcoding operations,
aiming at:
2

4. Optimized
Encoding Task
Scheduler for
Cloud and Fog
Environments
(OTEC)
OTEC
3

5. OTEC Architecture Overview
4

6. OTEC Architecture Overview
Mixed Integer Linear
Programming (MILP) model
5

7. Resource Allocation Example
7

8. Optimization Model
Cluster selection
One cluster per timeslot
Select the required number of instances
Cluster start and end times
Non-overlapping encoding timeslots
Time and cost
Total transcoding time
Total transcoding cost
Sets of constraints:
8

9. Objective Function
9
To optimize a multi-objective function representing a
linear combination of two metrics, total transcoding cost 𝜆
and total transcoding time 𝜃

10. Maximum possible cost
10
Objective Function
To optimize a multi-objective function representing a
linear combination of two metrics, total transcoding cost 𝜆
and total transcoding time 𝜃
Administration priority
𝛼 ∈ [0, 1]
Time-optimized (𝛼 = 0)
Cost-optimized (𝛼 = 1)
Maximum possible
transcoding time

11. Evaluation
Various administration priorities 𝛼
Various timeslot duration and number
of segments
Various segment transcoding times
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12. 12
Experimental Infrastructure

13. Administration Priority
Time-optimized results are 30.5% faster and 24.78% more expensive than the cost-optimized ones.
For 𝛼 = 0.5, there is a tradeoff between the total cost (0.0024 $/s) and transcoding time (6.99 s).
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14. Resource Allocation Results
14
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
10 segments

15. Timeslot Duration and Number of Segments
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
15

16. Timeslot Duration and Number of Segments
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
16

17. Timeslot Duration and Number of Segments
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
17

18. Timeslot Duration and Number of Segments
∼2.5 times
15%
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
18

19. Timeslot Duration and Number of Segments
∼5.24 times
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
19

20. Coordinator
4.5 times
8.31 times
Segment Transcoding Time
Time-optimized scenario
Coordinator on a local cloud instance
A four-core Intel core-i7 processor
32 GB of memory
370 instances
10 segments
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21. Conclusions
Proposed OTEC for optimized scheduling of adaptive transcoding processes in
Cloud and Fog environments
Proposed an MILP optimization model
Highlighted factors:
Administrator 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
transcoding time
Evaluated OTEC on a set of Cloud AWS and Fog Exoscale resource instances
Achieved that time-optimized scenarios are 30.5% faster and 24.78% more
expensive than the cost-optimized ones.
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22. Thank you
Have a
great day
ahead!
Institute of Information Technology (ITEC) Alpen-Adria-Universität Austria
samira.afzal@aau.at https://itec.aau.at/
APOLLO