The document proposes a two-sided matching model called CODA to allocate heterogeneous cloud-fog resources for data stream processing applications. CODA models applications as graphs of microservices and resources as nodes with computing and networking capabilities. It uses matching theory to match microservices to resources based on rankings to minimize completion time and network traffic. The document provides an example of CODA's matching algorithm, describes evaluation setups using simulation and a real testbed, and presents results comparing CODA to other approaches.

1. A Two-Sided Matching Model for Data
Stream Processing in the Cloud-Fog
Continuum
Narges Mehran, Dragi Kimovski, Radu Prodan
University of Klagenfurt, Klagenfurt, Austria

2. Introduction & motivation
CODA model
Example on CODA
Evaluation setups and results
Conclusion & future work

3. Introduction
• Latency-sensitive and bandwidth-intensive data stream processing services,
➢amongst the dominating traffic generators in today's world
• Processing such data streams in nearly real-time
• Requiring vast amounts of compute, storage and network resources in proximity of
data sources
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021. 3

4. …
CDC 1 CDC 2 CDC n
…
…
Big Data analysis
and memory
optimized
Compute optimized
GPU instances …
Cloud-Fog continuum
Micro DC 1 Micro DC n
• Micro or femto data centre
directly connected to base
station,
• Cisco router1,
• Cluster of:
✓ VMs running on workstations,
✓ Raspberry Pi,
✓ Nvidia Jetson Nano,
✓ Nano Pi.
https://lead-conduct.de/
4
1 https://developer.cisco.com/site/iox/
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

5. Motivation
• To manage:
➢heterogeneous Cloud-Fog continuum
➢resources allocation and application
deployment
➢strict latency and bandwidth requirements
5

6. Cloud-fOg Data stream
application mAtching
• Cloud-fOg to Data stream application mAtching (CODA):
• addresses the problem of deploying data stream processing applications on
heterogeneous resources,
• CODA approaches this problem using matching theory principles,
➢ application microservices:
✓ require lower completion time,
➢ computing resources:
✓ require lower traffic.
6
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

7. Application model
set of interconnected
microservices
Source and sink
microservices
Application edges
for data element
flowing among
microservices
Data source and sink
mi
mu
Dataui
Upstream
microservice of mi
7
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

8. Resource model
rj
8
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

9. Resource and
channel models
rq
rj
9
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

10. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
Ranking
10
m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r3
m5
r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1
m2 m5
Matching
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

11. Ranking methods
• We propose two ranking methods:
➢ microservice-side ranking that considers the microservice time;
➢ resource-side ranking that considers the residual bandwidth.
11
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

12. • Element processing time:
𝑡 𝑚𝑖, 𝑑𝑎𝑡𝑎𝑢𝑖 𝑥 , 𝑟
𝑗 =
𝐶𝑃𝑈(𝑚𝑖, 𝑑𝑎𝑡𝑎𝑢𝑖[𝑥])
𝐶𝑃𝑈𝑗
+
𝑑𝑎𝑡𝑎𝑢𝑖[𝑥]
𝐵𝑊𝑞𝑗
+ 𝐿𝐴𝑇𝑞𝑗
• Microservice time:
𝑇 𝑚𝑖, 𝑑𝑎𝑡𝑎𝑢𝑖 , 𝑟
𝑗 = ෍
𝑥=1
𝑠𝑖𝑧𝑒𝑢𝑖
𝑡 𝑚𝑖, 𝑑𝑎𝑡𝑎𝑢𝑖 𝑥 , 𝑟
𝑗
Microservice-side ranking
computation time transmission time and channel latency
Number of data elements
12
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

13. • Residual bandwidth:
𝑅𝑒𝑠𝑑𝐵𝑊
𝑗 𝑚𝑖, 𝑑𝑎𝑡𝑎𝑢𝑖 , 𝑟
𝑗 = 𝐵𝑊𝑞𝑗 − ෍
𝑥=1
𝑠𝑖𝑧𝑒𝑢𝑖
𝜆𝑢𝑖 . 𝑑𝑎𝑡𝑎𝑢𝑖[𝑥]
Resource-side ranking
ingress data rate & data stream
Bandwidth between resources rq and rj
13
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

14. Problem definition
• Resource allocation problem → as a matching game
14
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

15. Problem definition
• Resource allocation problem → as a matching game
15
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

16. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
An example on CODA Algorithm
16
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

17. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
17
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

18. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
18
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

19. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
19
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

20. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
20
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

21. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
21
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

22. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
22
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

23. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r4 r1 r3
m5
r1 r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1 m5 m4
m2 m5 m4
23
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

24. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r3
m5
r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1
m2 m5
24
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

25. m1
m3
m2
m4
r4 r2 r3
r1 r3 r2
r1 r2
r3
m5
r4 r2 r3
r1
r3
r2
r4
m2 m3 m1 m5
m2 m3 m5 m4
m3 m1
m2 m5
25
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.
An example on CODA Algorithm

26. Traffic sign classification in
video stream applications
Traffic sign classification in video stream applications
1) Encoding: encodes the raw video stream with
ffmpeg software suite with the H.264 codec.
2) Framing: divides the videos into frames by utilizing
OpenCV library.
3) Analysis: is an ML model to train a neural network
from 50,000 video frames of 43 traffic signs.
26
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

27. 1. iFogSim simulator
2. C3 Testbed:
➢m5a.xlarge AWS general purpose
instance
➢One twelve-core AMD Ryzen
Threadripper 2920X processor at
3.5GHz and 32GB of RAM
➢RPi and NVIDIA Jetson Nano as the one-
hop devices connected to data source
Experimental setups

28. Performance metrics
• Stream processing time: completion time of the msnk microservice;
μ(A)⊆R:
28
msrc m3
m2
m4 msnk 𝐶 𝐴, 𝑅 = 𝐶(𝑚𝑠𝑛𝑘, 𝑅)

29. • Stream processing time: completion time of the msnk microservice;
μ(A)⊆R:
• Total streaming traffic: total traffic aggregates the traffic across all
network channels:
29
msrc m3
m2
m4 msnk 𝐶 𝐴, 𝑅 = 𝐶(𝑚𝑠𝑛𝑘, 𝑅)
r1
r3
r2
r4
Performance metrics

30. • Stream processing time: completion time of the msnk microservice;
μ(A)⊆R:
• Total streaming traffic: total traffic aggregates the traffic across all
network channels:
30
msrc m3
m2
m4 msnk 𝐶 𝐴, 𝑅 = 𝐶(𝑚𝑠𝑛𝑘, 𝑅)
r1
r3
r2
r4
Performance metrics

31. Related work comparisons
• Three state-of-the-art approaches:
➢ Heterogeneous Earliest Finish Time–only Cloud (HEFT-oC)
➢ Response Time Rate with Region Patterns (RTR-RP)
➢ CloudPath
31
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

32. Simulation scenarios
• First scenario:
➢CPU load of {10000, 20000, 30000, 40000} MI,
➢Data size of dataui[x] = 10MB.
• Second scenario:
➢Data size of {0.1, 1, 5, 10} MB,
➢CPU load = 15000 MI.
32
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

33. Simulation
results

34. Real-testbed scenarios
• First scenario:
➢ encoding bitrate ∈ {200,1500,3000,6500,20000} kbps,
➢ dataui[x]=2560kB.
• Second scenario:
➢ dataui[x] ∈ {35,300,420,1350,2560} kB,
➢ bitrate= 20000kbps.
34
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

35. Real-testbed
results

36. Real-testbed scenarios
• First scenario:
➢ encoding bitrate ∈ {200,1500,3000,6500,20000} kbps,
➢ dataui[x]=2560kB.
• Second scenario:
➢ dataui[x] ∈ {35,300,420,1350,2560} kB,
➢ bitrate= 20000kbps.
36
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

37. Conclusion and future work
• Proposed CODA for running
stream processing applications on
Cloud-Fog resources,
• considered stream processing
time and the streaming traffic as
our main goals,
• achieved 11-45% lower stream
processing times and 1.3-20%
lower streaming traffic.
37
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

38. Conclusion and future work
• Future works:
➢ Investigating the scheduling
algorithm based on matching theory,
➢ Inspecting the dynamicity of
applications and resources.
• Proposed CODA for running
stream processing applications on
Cloud-Fog resources,
• considered stream processing
time and the streaming traffic as
our main goals,
• achieved 11-45% lower stream
processing times and 1.3-20%
lower streaming traffic.
38
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

39. References
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Transactions on Network and Service Management, vol. 16, no. 4, pp. 1624-1637, Dec. 2019.
2. Seyed Hossein Mortazavi, Mohammad Salehe, Carolina Simoes Gomes, Caleb Phillips, and Eyal de Lara. Cloudpath: A multi-tier cloud
computing framework. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pages 1–13, 2017.
3. Alexandre da Silva Veith, Marcos Dias de Assuncao, and Laurent Lefevre. Latency-aware placement of data stream analytics on edge
computing. In International Conference on Service-Oriented Computing, pages 215–229. Springer, 2018.
4. David Gale and Lloyd S Shapley. College admissions and the stability of marriage. The American Mathematical Monthly, 69(1):9–15,
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5. Marcos Dias de Assuncao, Alexandre da Silva Veith, and Rajkumar Buyya. Distributed data stream processing and edge computing: A
survey on resource elasticity and future directions. Journal of Network and Computer Applications, 103:1–17, 2018.
6. S Segvic, K Brkic, Z Kalafatic, V Stanisavljevic, M Sevrovic, Damir Budimir, and I Dadic. A computer vision assisted geoinformation
inventory for traffic infrastructure. In 13th International IEEE Conference on Intelligent Transportation Systems, pages 66–73. IEEE, 2010.
7. Ali Reza Zamani, Mengsong Zou, Javier Diaz-Montes, Ioan Petri, Omer Farooq Rana, Ashiq Anjum, and Manish Parashar. Deadline
constrained video analysis via in-transit computational environments. IEEE Transactions on Services Computing, 2017.
8. Dragi Kimovski, Roland Matha, Josef Hammer, Narges Mehran, Hermann Hellwagner, and Radu Prodan. Cloud, fog or edge: Where to
compute? IEEE Internet Computing, 2021.
9. Mahsa Ehsanpour, Siavash Bayat, and Ali Mohammad Afshin Hemmatyar. An efficient and social-aware distributed in-network caching
scheme in named data networks using matching theory. Computer Networks, 158:175–183, 2019.
39
Mehran, Kimovski, Prodan. “A Two-Sided Matching Model for Data Stream Processing in the Cloud-Fog ..,” CCGrid2021.

40. Thanks for your attention ☺
A Two-Sided Matching Model for Data
Stream Processing in the Cloud-Fog
Continuum
Narges Mehran, Dragi Kimovski, Radu Prodan
Klagenfurt University, Klagenfurt, Austria
Email: Narges.Mehran@aau.at
Code: https://github.com/SiNa88/CODA