NeuRoute: Predictive Dynamic Routing for Software-Defined Networks

NeuRoute: Predictive Dynamic Routing for
Software-Defined Networks
Abdelhadi Azzouni 1 Raouf Boutaba 2 Guy Pujolle 1
1Université Pierre et Marie Curie, France
2University of Waterloo, Canada
ManSDN/NFV, 2017
a.azzouni (LIp6, UPMC) NeuRoute: Predictive Dynamic Routing for Software-Defined NetworksManSDN/NFV, 2017 1 / 27

Outline
1 Introduction
Motivation
2 The Dynamic Routing Problem
3 NeuRoute
Traffic Matrix Estimator
Traffic Matrix Predictor
Traffic Routing Unit
Learning Algorithm and Optimization Algorithm
4 Implementation
5 Evaluation Results
6 Conclusion

Outline
1 Introduction
Motivation
3 NeuRoute
4 Implementation
6 Conclusion

Introduction
The number of connected devices (Internet of Things; IoT) worldwide
(www.statista.com)

Introduction
Explosive number of connected
devices
QoS requirements going very
high
High pressure on carrier
operators to increase their
network capacity

Introduction
The common practice to ensure a good QoS so far is to
over-provision network resources.
Operators over-provision a network so that capacity is based on peak
traﬃc load estimates.
Although this approach is simple for networks with predictable peak
loads, it is not economically justiﬁed in the long-term.

Introduction
Why don’t operators implement QoS algorithms?

Introduction
Why don’t operators implement QoS algorithms?
The decentralized nature of traditional networks makes it very hard to
implement efficient and homogeneous mechanisms to differentiate traffic
Solution: SDN

The Dynamic Routing Problem
We formulate MT-MC-DRP as a succession of two linear problems (LPs): A
Constrained-Maximum-Flow LP (CMaxF-LP) and a Constrained-Minimum-Cost
LP (CMinC-LP).
CMaxF-LP
maximize(
f ∈F
r
f
in(df )) (1)
subject to:
r
f
(l) ≥ 0 ∀f ∈ F, ∀l ∈ L
f
(2)
r
f
(l) ≤ C(l) ∀f ∈ F, ∀l ∈ L
f
(3)
f ∈F
r
f
(l) ≤ C(l) ∀l ∈ L (4)
r
f
in(v) = r
f
out (v) ∀f ∈ F, ∀v ∈ V
f
− {sf , df } (5)
r
f
in(sf ) = 0 ∀f ∈ F (6)
r
f
out (df ) = 0 ∀f ∈ F (7)
r
f
out (sf ) ≤ R
f
∀f ∈ F (8)
r
f
out (sf ) ≥ N
f
∀f ∈ F (9)
CMinC-LP
minimize(
f ∈F l∈L
r
f
(l) × Θ(l)) (10)
subject to:
r
f
out (sf ) = Π
f
+ / − ∀f ∈ F, ∀l ∈ L
f
(11)
f ∈F
r
f
(l) ≤ C(l) ∀l ∈ L (12)
r
f
in(v) = r
f
out (v) ∀f ∈ F, ∀v ∈ V (13)
r
f
in(sf ) = 0 ∀f ∈ F (14)
r
f
out (df ) = 0 ∀f ∈ F (15)
2

The Dynamic Routing Problem
As we mentioned before, there are a lot of approximative solutions to the
dynamic routing problem. We take a diﬀerent approach.

NeuRoute
The Routing Problem As A Game
Machine Learning is getting so good!
AlphaGo wins against world’s
best Go human player
DeepMind has been breaking
records in Atari games
OpanAI’s program beats world’s
best DOTA2 human player

NeuRoute
A network Traffic Matrix (TMt) presents the traffic volume between all
pairs of origin-destination (OD) nodes of the network at a certain time t
TM (Traffic Matrix) Estimator
continuously collects traffic
matrix data and store it
At each time instant t, TM
Predictor predicts future traffic
matrix TMt+1 using traffic
history
The TE (Traffic Engineering)
Unit is responsible for
calculating the optimal routing
to route TMt+1

Outline
1 Introduction
Motivation
3 NeuRoute
4 Implementation
6 Conclusion

In the current implementation, NeuRoute uses
a variant of openMeasure (Chang. et al.
Infocom WKSHPS’16) to estimate traﬃc
matrix.
The idea is to measure heavy hitters ﬁrst

Outline
1 Introduction
Motivation
3 NeuRoute
4 Implementation
6 Conclusion

TM predictor uses LSTM (Long Short Term
Memory) Neural Networks to predict time
series of TM
Sliding window technique: At eat time t, TM
Predictor uses n measured TMs to predict
TMt+1
Extensive details on TM predictor in our paper NeuTM, submitted to NOMS’17

Outline
1 Introduction
Motivation
3 NeuRoute
4 Implementation
6 Conclusion

At each time t, TE Unit receives TMt+1 as
input and outputs the optimal routing paths for
it
TE Unit is a feed forward deep neural network
TE Unit learns network characteristics from
TM data and matches them to optimal routing
paths

Deep Feed Forward Neural Networks (DFFNN or DNN)
DNNs are currently the most successful
machine learning technique for solving a
variety of tasks including language
translation, image classification and
image generation.
We approach the routing problem as an
image classification problem, where the
traffic matrices are the images and the
routing paths represent the output
classes.

Learning Algorithm
We use the Backpropagation (BP) as a
learning algorithm
BP is based on the chain rule
dy
dx
=
dy
du
.
du
dx
(16)
The chain rule is applied backward,
calculating the error at each level and
adjusting the connections’ weights
This way, the global prediction error is
minimized
We use Adam optimizer (an modiﬁed
version of gradient descent) to compute
the error

Implementation
We use data from the GÉANT network: 23
nodes and 38 links
The TE Units neural network is implemented
using Keras on top of Googles TensorFlow
We implemented the GÉANT topology as an
SDN network using Mininet setting link
capacities at 10Mbps Figure: The GÉANT
topology

Implementation
Learning-data generation
Data generation. In order to generate the learning routing-data, we
applied a heuristic (Aleksander. et al) on GANTs traﬃc matrices
We obtained a data set of 10000 samples (traﬃc matrix+network
state, near optimal path) that we split to training data set of 7000
samples and test data set of 3000 samples.
Figure: The cross validation technique

Results
Figure: Mean Squared Error over
number of hidden layers
Figure: Training time over number of
hidden layers
Optimal number of hidden layers: 5 or 6

Evaluation Results
Figure: MSE over number of hidden
nodes
Figure: Training time over number of
hidden nodes
Optimal number of hidden nodes is 100*6=600 (6 layers)

Evaluation Results
Figure: Accuracy over number of
training epochs
Finally, the execution time of the TE unit is 25ms. 1/5 the execution time
of the heuristic ( 125ms)

Conclusion
NeuRoute is a predictive dynamic routing framework
TM estimator is based on OpenMeasure
TM predictor uses LSTM recurrent neural network
TE unit uses a deep neural network to match traﬃc matrices to
optimal paths
Estimation → Prediction → Routing

For Further Reading I
A. Author.
Handbook of Everything.
Some Press, 1990.
S. Someone.
On this and that.
Journal of This and That, 2(1):50–100, 2000.

NeuRoute: Predictive Dynamic Routing for Software-Defined Networks

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