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End-to-end Self-Diagnosis of Programmable Networks
8th July 2015
José Manuel Sánchez Vilchez
Supervisor: Imen Grida Ben Yahia,, Orange Labs, Issy Les Molineaux, France
Thesis Director: Noël Crespi, Telecom SudParis, Evry, France
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Outline Context Scientific contributions Results Conclusion and future lines
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Outline Context Scientific Contributions Results Conclusion and future lines
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Programmable Networks
Existing recovery solutions for SDIs:
•mostly OpenFlow-based
•only handle physical faults in the data layer
•only a few solutions focus on the control layer and controller
•root cause analysis is not covered yet
decentralization controller
placement
Single point of failure: SDN controller Security and scalability
redundancy
Two challenges concerning management of programmable networks
Network
dynamicity Resilience
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Problem statement
End-to end diagnosis in combined SDN and NFV infrastructures
SDN is the underlying layer of NFV based services
The SDN controller dynamically interconnects the VNFs
Radio
Access Network VNF 4
VNF 5
SERVER SERVER
client(t+t1)
client(t+t2)
Virtualized Content Network
VNF2
Virtualized Core Network
SDN controller
VNF2 SERVER VA2 VNF 5 VA1 VAP forwarding graph of client(t+t1)
VA2 VA1 VAP common network functions to all clients
SERVER VA2 VNF 4 VA1 VAP forwarding graph of client(t+t2)
network functions
and services
network
topology
Network
dynamicity Resilience
Two challenges concerning management of programmable networks
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Outline Context Scientific Contributions • Self-Healing arhitecture • Self-Diagnosis module • Proposed templates Results Conclusion and future lines
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Scientific Contributions
Self-diagnosis based on a self-modeling approach
1)network topology
self-healing architecture
with the following characteristics:
1) southbound independent
2) cross layer
Network
dynamicity Resilience
Two challenges concerning management of programmable networks
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Self-Healing cross-layer architecture for SDN
topology2 topology1
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Topology-Aware Self-diagnosis:
-Root Cause Analysis from the network topology and type of control
-The diagnosis model contains dependencies among network nodes and links
Based on two blocks:
-Self-modeling: Topology Interpreter + Dependency Graph Building
-Root-Cause analysis based on a model-based Bayesian Networks algorithm
Topology-Aware Self-Diagnosis framework
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Topology-Aware Self-Modeling approach
Definition of a set of adaptable fine-
grained templates to model the
network dependencies at physical and
logical layers:
-Inside the SDN nodes (sub-
components)
-Among the SDN nodes (network
topology)
The dependency graph of the
network is automatically built by
combining the dependency graphs of
the discovered network nodes and
links
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Topology-Aware Self-Modeling approach
Composition of Global Dependency Graph
Element classification
Template instantiation
Dependency Graph extraction
nodes links
addition of GNnto G addition of GLnto G
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2
3
4
GNn GLn
Topolo
gy
Inte
rpre
ter
De
pe
nd
ency
Gra
ph
Bu
ildin
g
hosts controllers switches control access interswitch
S1
TSWITCH
S1
S2
TSWITCH
S2
S3
TSWITCH
S3
C1
TCONTROLLER
C1
H1
THOST
H1
CL3
TCONTROL
CL2
TCONTROL
CL1
TCONTROL
IL2
TCORE
IL1
TCORE
AL2
TACCESS
IL3
TCORE
AL1
TACCESS
H2
THOST
H2
TNn TLn
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Outline Context Proposals Results -Topology-Aware Self-Modeling
-Topology-Aware Root Cause Analysis
Conclusion and future lines
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Results: Topology-Aware Self-Modeling Self-Modeling Validation: dynamic network topologies and types of control
control: in-band, network topology: ring (5 switches, 2 hosts)
control: out-of-band, network topology: ring (5 switches, 2 hosts)
Ci: controller ALi: access link
Hi: host CLi: control link Legend
ILi: inter switch link Si: switch
C1
C1 CL1 S1 S1 S2 S3 S4
CL1 S1 S2 S3 S4 S5 CL2 CL3 CL4 CL5
IL1 IL2 IL3 IL4 AL1 AL2
IL1 IL2 IL3 IL4 AL1 AL2
H2 H1
H2 H1
number of generated vertices in the dependency graph: 73
number of generated vertices in the dependency graph: 65
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Results: Topology-Aware Root Cause Analysis faulty controller
C1 CL1 S1 KL1 AL2 H2 H1 AL1 S2 CL2
up
down
Evidences: Root Cause
Probability (%):
0.9 0.9
0.4 0.4 31.4 0.6 0.6
31.4
31.4
0.4
0.6
0.4
0.6
H1 H2
S1 S2
C1
CL1 CL2
AL2
KL1
AL1
1 1
1.9
94.2
1.9
Root Cause node : C1
Node’s subcomponents:
CPU problem (31.4%)
Controller APP not initiated
(31.4%)
Controller APP not configured
(31.4%)
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Results: Self-Modeling Evaluation Performance Self-Modeling of tree, linear, clos-like, fat tree and ring topologies with in-band an out-of-band control
Analysis of performance of the self-modeling algorithm as a function of the number of network elements discovered.
Exponential trend in the growth of self-modeling time with the number of elements for linear and tree topologies (< 30 seconds for both cases).
0 50 100 150 200 250 300 350 400 450 5000
5
10
15
20
25
Linear Topology
Tree Topology
time to generate the dependency graph (seconds)
50 100 150 200 250 300 350 400 450 500
Number of discovered
network elements
(nodes and links)
20
25
5
10
15
0
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Results: Topology-Aware Root Cause Analysis simultaneous link failures in control and data plane
H1 H2
S1 S2
C1 CL1
CL2
AL2
IL1
AL1
1.8
1.1
31.1
31.1
1.1
31.1
1.8
0.9
C1 CL1 S1 IL1 AL2 H2 H1 AL1 S2 CL2
31.1 31.1
0.9
0.5 0.5
31.1
0.6
0.9
0.3 0.3 0.3
0.6
0.6
0.6 0.6
Root Causes:
CL1, AL1, AL2
up
down
Evidences: Root Cause
Probability (%):
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Outline Context
Proposals Results Conclusion and future lines
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Conclusion and future lines Work done
Self-diagnosis framework to empower model-based diagnosis in SDN and NFV scenarios in a controller's domain. It utilizes a self-modeling approach based on a set of predefined fine-grained templates
Results
Evaluation of scalability of self-modeling algorithm over different topologies until 500 network elements per controller’s domain (<30 seconds)
Future work
Extension of this Self-modeling mechanism to encompass different network topologies of different controller’s domains
Adoption of learning mechanisms for automatically generation of templates of new equipment added to the network
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Thank you for your attention!
Any questions?