The document discusses the transition from cloud computing to fog computing. Fog computing decentralizes computing to the edge of networks in order to address issues like high latency and huge data volumes from IoT devices. It describes how Kubernetes works well for containers but is not optimized for proximity-aware routing needed in fog. Algorithms are presented for optimizing pod placement in fog to minimize latency. Models for optimizing stream processing across fog nodes based on computational complexity and network latency are also introduced. The talk concludes by discussing an ongoing research project called FogGuru that is conducting real-world fog computing experiments.

1. From Cloud to Fog:
The Tao of IT Infrastructure Decentralization
Guillaume Pierre
Keynote at the IEEE CloudNet Conference, Nov 4th 2019
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2. End users want interactivity
IoT devices produce huge volumes of data
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5. Latency
Bandwidth
Cost
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6. Fog Computing
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7. Fog Computing
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8. Fog Computing
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9. Fog Computing
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10. Fog Computing
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11. Fog = Cloud: the dierence is the network
Typical Cloud platform
Few data centers
High-performance networks
Cloud resource location is
(mostly) irrelevant
Resources close to each
other, far from the users
Typical Fog platform
Lots of points-of-presence
Heterogeneous networks
Fog resource location is
extremely important
Resources close to the
users, far from each other
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12. Experimentation platform
⇒
the fridge
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13. The Tao of IT Infrastructure Decentralization
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14. The Tao of IT Infrastructure Decentralization
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15. The Tao of IT Infrastructure Decentralization
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16. Kubernetes is a great platform...
Container-based ⇒ lightweight, can run on a Raspberry Pi
Pods and services ⇒ easy to deploy and (re-)scale applications
Integrated autoscaling ⇒ ready for uctuating workloads
Designed around feedback control loops ⇒ simple and robust
Large community of users and developers ⇒ we can nd help
whenever necessary
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17. ... but it is not ready for the fog
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18. ... but it is not ready for the fog
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19. ... but it is not ready for the fog
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20. ... but it is not ready for the fog
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21. ... but it is not ready for the fog
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22. How Kubernetes routes end user requests
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23. How Kubernetes routes end user requests
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24. Kubernetes load-balances trac across pods
Let's issue lots of requests on node #7 (which contains a pod)
Other pods are available:
At node #6: 2ms
At nodes #4 and #5: 4ms
At nodes #2 and #3: 6ms
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25. If we make request routing location-aware
Proximity-Aware Trac Routing in Distributed Fog Computing Platforms. Ali Fahs and Guillaume Pierre.
In Proceedings of the IEEE/ACM CCGrid Conference, May 2019.
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26. Great.
But. . .
We still need to place pods close to the end users
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28. Pod placement is an NP-hard problem
Place r replicas among n fog nodes to minimize user-pod distance
⇒ r
n = n!
r!(n−r)! possible combinations to explore
When n = 10 and m = 100 this makes 17,310,309,456,440 combinations
Heuristic: Let's nd any reasonably good pod placement
Reasonably good == at least F% of user requests served within X ms
(e.g., F = 99.9% and X = 5 ms)
We can explore the space of placements (more or less) randomly until:
we nd a reasonably good placement
or
a timer expires
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29. Scheduling pods
Create Pods Schedule Pods Deploy Pods
Request a
Deployment
Deployement
Controller
Pod objects
Created
Kubernetes
Scheduler
Pods
Assigned
Running
Pods
Kubelet
S4
S1 S2
S3 S4
S5 S6
S7 S8
S1
S3 S4
S8
S4
S8
S1
S3
Score: 8
Score: 7
Score: 4
Score: 3
All Schedules Feasible Schedules Ranked Schedules
Final
Solution
Filter Score
Rank
Assign
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30. Pod placement performance
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
n=50 n=100 n=200 n=300
93
95
97
99
98.3
98.8
99.3
99.8
99.61
99.71
99.81
99.91
99.84
99.87
99.90
99.93
K
R
H1
H2
µP%
Algorithm
(a) Proximity as function of dierent algorithms
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
n=50 n=100 n=200 n=300
5 7 9 11 3.4 4.0 4.6 5.2 1.7 2.0 2.3 2.6 1.3 1.5 1.7
K
R
H1
H2
µI%
Algorithm
(b) Fairness as function of dierent algorithms
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32. Stream data processing
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33. Stream data processing
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36. With a bandwidth-optimized pre-combined operator
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38. So Si
Op1
Op2
Op3
(a) Logical graph of operators.
SiOp3
Op2
So
So
Op1
Op1
Op1
(b) Replicated data source and operator.
(c) Geo-distributed operator replicas in a Fog computing environment.
Can we model the performance of stream processing in the fog?
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39. Performance model for a single operator
We can model an operator's throughput with just 3 parameters:
Πn =
α
nβ
+ γ × NDmax
α captures the operator's computational complexity
β captures the parallelism overhead of Apache Flink
γ captures the impact of network latency
With this model we can predict the
throughput with error ≈ 2%
0
10000
20000
30000
40000
50000
60000
70000
80000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Measured Points
ProcessingTime(ms)
Number of Replicas
Basic Measurments
3 Measurments Prediction
Simple operators:
replicated and distributed
KeyBy operator
Model calibration
Model composition
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41. The FogGuru project
www.fogguru.eu
Training the Next Generation of European Fog Computing Experts
H2020 European Industrial Doctorate Maria Skªodowska-Curie project
Eight talented PhD students conducting research on fog computing
Real-world experimentation in Valencia (Spain), starting. . . today
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42. Real-world experimentation
Technical goals:
Detect water leaks
Optimize water
distribution
Inform citizens about
their consumption
. . . ?
Societal goals:
Validate research in real-life settings
Develop fog application blueprints
Meet real customers and citizens
Benet the city of Valencia, its
citizens and its companies
Disseminate the project's results
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44. When a Cloud surrounds you it becomes Fog.
This is only the beginning of the journey. . .
www.fogguru.eu
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45. Credits
The Myriads team at IRISA:
The FogGuru project:
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