A decentralized cloud for IoT computations. Presented at IEEE Cloudification of IoT (CIoT) - 2016 in Paris

1. Edge-Fog Cloud: A Distributed
Cloud for Internet of Things
Computations
Nitinder Mohan, Jussi Kangasharju
Department of Computer Science, University of Helsinki, Finland
{firstname.lastname@cs.helsinki.fi}
Conference on Cloudification of Internet of Things (CIoT) – 2016
Paris

2. Rise of connected IoT devices
Projected number of IoT devices Average cost of a sensor
Broadband by the numbers (NCTA), https://www.ncta.com/broadband- by-the-numbers
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3. Computational Data Centers
https://cloud.google.com/about/locations/
3

4. Problem: Network!
 High transport cost
 High data volume
 High network latency
https://cloud.google.com/about/locations/
4
Computational Data Centers

5. Fog Cloud Computing
Cloud
Fog
Devices
Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013). Mobile fog. Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing - MCC ’13
Processing-capable network resources augment the cloud
4

6. Edge Cloud Computing
Processing-capable, voluntary, user-controlled devices augment the cloud
Lopez, P. G., Montresor, A., Epema, D., Iamnitchi, A., Felber, P., & Riviere, E. (2015). Edge-centric Computing : Vision and Challenges. Acm Ccr, 45(5), 37–42.
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7. Edge & Fog Cloud: Problem
Computation requires routing data to a central cloud!
Cloud
Fog
Devices
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8. Edge-Fog Cloud

9. Architecture
Data
Store
Fog
Edge
Edge
 Collection of devices:
i. Loosely-coupled
ii. Voluntary
iii. Human operated
 1-2 hops away from sensors & clients
 Ad-hoc device-to-device connectivity
within layer
 Varying processing capability
e.g. desktops, laptops, workstations,
nano data centers etc. 8

10. Data
Store
Fog
Edge
Architecture
Fog
 Network devices with high compute
capability
 Manufactured, managed and deployed
by cloud vendors such as CISCO*
 Lies farther from sensors but closer to
core
 Dense connectivity within layer
 Reliable connectivity to Edge
e.g. routers, switches etc.
*CISCO, “Cisco fog computing solutions: Unleash the power of the Internet of Things (whitepaper),” 2015
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11. Data
Store
Fog
Edge
Architecture
Data Store
 Data archival and storage
 No computation on data
 Reliability and ease-of-access to
data in Edge and Fog layers
8

12. Data
Store
Fog
Edge
Benefits
1. Reduced network load
2. Native support for mobility
3. Context in computation
4. No single point-of-failure
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13. Workload Assignment

14. D1 D2 D3
D4 D5
1
4 34
1
Edge-Fog Cloud
J1 J2 J3
J4
J5
Job Graph
*Haubenwaller, Andreas Moregård, and Konstantinos Vandikas. "Computations on the Edge in the Internet of Things." Procedia Computer Science 52 (2015)
Network Only Cost Assignment*
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15. J1 J2 J3
J4
J5
J4 J5 J3
J1
J2
J2 J3 J4
J1
J5
I. Naïve Implementation
Iterative Search
𝒩 devices
𝒩 jobs
Worst Case: O(𝒩!)
D1 D2 D3
D4 D5
1
4 34
1
Network Only Cost Assignment
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16. Network Only Cost Assignment
D1 D2 D3
D4 D5
1
4 34
1
J1 J2 J3
J4 J5
99 1 8 4 5
1 99 7 5 4
8 7 99 4 3
4 5 4 99 1
5 4 3 1 99
0 1 0 1 0
1 0 1 0 1
0 1 0 0 0
1 0 0 0 1
0 1 0 1 0
Dconn[ i, j ] =
Jconn[ i, j ] =
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17. Network Only Cost Assignment
99 1 8 4 5
1 99 7 5 4
8 7 99 4 3
4 5 4 99 1
5 4 3 1 99
Dconn[ i, j ] =
0 1 0 1 0
1 0 1 0 1
0 1 0 0 0
1 0 0 0 1
0 1 0 1 0
Jconn[ i, j ] =
II. Quadratic Assignment Problem
Minimize:
NP-hard!
• Approximated using Kuhn-
Munkres or GLB bounds
• Optimal solution not guaranteed
𝑎(𝑖,𝑗)∈𝐴
𝐽𝑐𝑜𝑛𝑛 𝑖, 𝑗 ∗ 𝐷𝑐𝑜𝑛𝑛(𝑓 𝑖 , 𝑓(𝑗))
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18. Least Processing Cost First (LPCF)
Device Processing Power [Dproc(i)]
D1: 3 D2: 2 D3: 2
D4: 5 D5: 6
1
4 34
1
J1: 4 J2: 2 J3: 5
J4: 4 J5: 2
Job Size [Jsize(i)]
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D1 D2 D3
D4 D5
1
4 34
1
J1 J2 J3
J4
J5

19. Least Processing Cost First (LPCF)
D1:3 D2:2 D3:2
D4:5 D5:6
1
4 34
1
J1:4 J2:2 J3:5
J4:4 J5:2
3 2 2 5 6
4 2 5 4 2
Dproc [i] =
Jsize [i] =
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20. Least Processing Cost First (LPCF)
3 2 2 5 6
4 2 5 4 2
Dproc [i] =
Jsize [i] =
I. Optimize Processing Cost
Minimize:
Linear Assignment Problem
• Solved using Kuhn-Munkres/
Hungarian algorithm
• Optimal solution guaranteed in
O(n3)
𝑖,𝑗∈𝐴
𝐶
𝐽𝑠𝑖𝑧𝑒(𝑖)
𝐷 𝑝𝑟𝑜𝑐(𝑗)
𝑥𝑖𝑗
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21. Least Processing Cost First (LPCF)
I. Optimize Processing Cost
Minimize:
Linear Assignment Problem
• Solved using Kuhn-Munkres/
Hungarian algorithm
• Optimal solution guaranteed in
O(n3)
𝑖,𝑗∈𝐴
𝐶
𝐽𝑠𝑖𝑧𝑒(𝑖)
𝐷 𝑝𝑟𝑜𝑐(𝑗)
𝑥𝑖𝑗
D1:3 D2:2 D3:2
D4:5 D5:6
1
4 34
1
J1:4 J2:2 J5:2
J4:4 J3:5
Least Processing Cost: 4.966
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22. Least Processing Cost First (LPCF)
II. Create sub-problem space
Edge-Fog Cloud composes of
several homogeneous devices
running homogeneous jobs
New Assignment Calculation:
1. Same processing power
→ interchange jobs
2. Same job size
→ interchange devices
D1:3 D2:2 D3:2
D4:5 D5:6
1
4 34
1
J1:4 J2:2 J5:2
J4:4 J3:5
Least Processing Cost: 4.966
J1:4 J5:2 J2:2
J4:4 J3:5
J4:4 J5:2 J2:2
J1:4 J3:5
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23. Least Processing Cost First (LPCF)
D1 D2 D3 D4 D5
1. J1 J2 J5 J4 J3
2. J1 J5 J2 J4 J3
3. J4 J5 J2 J1 J3
4. J4 J2 J5 J1 J3
Least Processing Cost: 4.966
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II. Create sub-problem space
Edge-Fog Cloud composes of
several homogeneous devices
running homogeneous jobs
New Assignment Calculation:
1. Same processing power
→ interchange jobs
2. Same job size
→ interchange devices

24. Least Processing Cost First (LPCF)
III. Account Network Cost
1. Compute network cost of
each assignment
2. Choose the assignment
with least network cost
D1 D2 D3 D4 D5
1. J1 J2 J5 J4 J3
2. J1 J5 J2 J4 J3
3. J4 J5 J2 J1 J3
4. J4 J2 J5 J1 J3
𝐽𝑐𝑜𝑛𝑛 𝑖, 𝑗 ∗ 𝐷𝑐𝑜𝑛𝑛(𝑓 𝑖 , 𝑓(𝑗))
Least Processing Cost: 4.966
N/W
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27
19
28
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25. Least Processing Cost First (LPCF)
Advantages
1. Computed assignment has least processing cost and
almost-optimal network cost
2. Task assignment accounts for processing cost of task
deployment
3. Assignment solution is guaranteed in polynomial time
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26. Evaluation

27. Edge-Fog Cloud Simulator
Python-based Edge-Fog Cloud Simulator
1. Generates:
i. Edge and Fog node graphs with device
processing and network costs
ii. Job node graphs with variable job sizes
2. Incorporates LPCF for assignment computation
3. Open Source
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28. LPCF vs NOC
Least Processing Cost
First
Network Only Cost
*solver available from QAPLIB, http://anjos.mgi.polymtl.ca/qaplib/
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Edge-Fog Cloud Simulator
+
LPCF Solver
Edge-Fog Cloud Simulator
+
Kuhn-Munkres Solver*

29. LPCF vs NOC
I. Assignment computation time
1 hour
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30. LPCF vs NOC
II. Network cost analysis
No time bound Time bounded
~10%
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31. LPCF vs NOC
III. Processing cost analysis
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32. Discussion

33. Q. How well connected should EF nodes be?
~21%
~17%
~9%
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34. Q. How does deployed job impact overall cost?
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35. Conclusion
Our contributions in this work are:
1. Formal architecture of Edge-Fog cloud
2. LPCF algorithm for assigning tasks on EF cloud
3. Open source Edge Fog cloud simulator & LPCF solver
4. Deployment analysis of Edge Fog cloud
Source code available at: www.github.com/nitinder-mohan/EdgeFogSimulator
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36. Backup

37. LPCF Search Space Reduction
Topology Size 5 10 15 30 60 100 150
Original Space 5! 10! 15! 30! 60! 100! 150!
LPCF Space 1! 3! > 4! > 5! > 7! > 8! > 9!
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38. EF Cloud Simulator Parameters
Property Value
Total number of devices/jobs Experiment Specific
Number of Edge devices 60% of total
Number of Fog devices 40% of total
Processing power of an Edge device 2-5
Processing power of a Fog device 7-9
Connection density of Edge layer (0-1) 0.2
Connection density of Fog layer (0-1) 0.6
Connection density between Edge and Fog layer (0-1) 0.5
Lowest job size in pool 2
Highest job size in pool 6
Inter-dependence density between jobs (0-1) 0.2
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