Introduction Problem Proposed Approach Solution Model Evaluation Conclusion A Connectionist Approach to Dynamic Resource Management for Virtualised Network Functions Rashid Mijumbi * , Sidhant Hasija * , Steven Davy * , Alan Davy * , Brendan Jennings * and Raouf Boutaba † * Telecommunications Software and Systems Group, Waterford Institute of Technology, Ireland † D.R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada Montreal, Canada, November 1, 2016
Transcript
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
A Connectionist Approach to Dynamic ResourceManagement for Virtualised Network Functions
Rashid Mijumbi∗, Sidhant Hasija∗, Steven Davy∗, Alan Davy∗,Brendan Jennings∗ and Raouf Boutaba†
∗Telecommunications Software and Systems Group, Waterford Institute ofTechnology, Ireland
†D.R. Cheriton School of Computer Science, University of Waterloo, Waterloo,Ontario, N2L 3G1, Canada
Montreal, Canada, November 1, 2016
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Presentation Outline
1 Introduction: Network Functions Virtualisation
2 Problem: Efficient vs Reliable Resource Management
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Network Functions Virtualisation
Increasing CAPEX and OPEX
The short lifetime of the NAs leads to increased CapitalExpenses (CAPEXs).
When NAs are specialised, they require specialisedmaintenance and limits flexibility, leading to increasedOperating Expenses (OPEXs).
Declining Revenues
Competition with over-the-top providers
Inability to quickly provide new services
Separation between infrastructure and ServiceOptimization of resource Usage and routing beyond BGP
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Network Functions Virtualisation
Physical Resources
Virtual Resources
Services
Network Function Virtualization Infrastructure
Man
age
me
nt
and
Orc
he
stra
tio
n
Computing, Storage, Network Resources
Virtual Network Functions
Computing, Storage, Network Resources
Man
age
me
nt
and
Orc
he
stra
tio
n
VNF 1 VNF 2 VNF 3 VNF n. . .
.Source: R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, R. Boutaba, ”Network FunctionVirtualization: State-of-the-art and Research Challenges”, IEEE Communications Surveys and Tutorials. 2016.
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Problem: Efficiency vs Reliability
NFV Essential for 5G, Supporting Critical Applications
NFV will be an important building block for 5G
5G is expected to support critical infrastructure
Efficiency and reliability are important KPIs for 5G
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
State-of-the-art
1 High VM provisioning time threatens reliability in criticalapplications such as M2M
1
10
100
1000
1 2 4 8 16 32
Number of Virtual Machines
Tota
l Pro
visi
onin
g Ti
me
(s)
Eucalyptus OpenStack OpenNebula
.Adapted from: Mike Jones et al. ”Scalability of VM Provisioning Systems”, 20th Annual IEEE High PerformanceExtreme Computing Conference(HPEC), September 2016, Waltham, MA USA.
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Our Proposal
Objective
Predict VNF Resource Requirements so as:
To avoid resources are not unnecessarily kept active/standbyWhile ensuring reliable performance
Idea
Topology-aware Resource Management
Motivation: VNFC Dependencies
Virtualization container such as a
VM
VNFC 1
VNF 1 VNF 2
VNFC 1
VNFC 2 VNFC 3
VNF 4
VNFC 1
VNF 3
Service Function Chain based on Virtualised Network Functions
VNFC 3 VNFC 4
VNFC 2 VNFC 5
𝑛2
𝑛3 𝑛4
𝑛5
𝑙32
𝑙21
𝑙14
𝑙46𝑙31
𝑙15
𝑙51𝑛0
𝑛7
𝑛8
VNF 1
VNF 2
VNF 3
VNF 4
𝑛5
𝑙41
𝑛6𝑛0
𝑙41
𝑙4
𝑥4
𝑥1
𝑙1
𝑙14
𝑙31
𝑙3𝑥3
𝑙13
VNFC 𝑛1
Neighbourhood of VNFC 𝑛1
𝑛1
𝑙12
𝑙13𝑙23
𝑙03
VNFC 1
VNF 2𝑛2
VNFC State VNFC Features
𝑛3
𝑛1
𝑠3
𝑓3
𝑠2
𝑓2
𝑛4
𝑛5
𝑠1𝑓1
𝑠1 𝑓1
𝑠3𝑓3
𝑠2 𝑓2
𝑠4 𝑓4
𝑠1 𝑓1
𝑠1𝑓1
𝑠5𝑓5
𝑛1
𝑠1 𝑓1
ℎ𝑤
𝑔𝑤
o1
𝑛5𝑠5 𝑓5
𝑔𝑤
ℎ𝑤
𝑛4
𝑠4 𝑓4
ℎ𝑤
𝑔𝑤
o4
o5
𝑛2𝑠2 𝑓2
𝑔𝑤
ℎ𝑤
𝑛3
𝑠3 𝑓3
ℎ𝑤
𝑔𝑤
o3
o2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤𝑥2
𝑥3
𝑥1
𝑥5
𝑥4
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤. . .
. . .
. . .
. . .
. . .
𝑠5
𝑠1
𝑠3
𝑠2 o2
o3
o1
o5
o4
𝑖0 𝑖1 𝑖2 𝑖𝑇
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑠4
𝑓2
𝑓4
𝑓5
𝑓1
𝑓3
VNFC 1
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Graph Neural Networks (GNN)
A supervised learning model aimed at solving problems in thegraphical domain.
Node, 𝑛4 Node, 𝑛3
Node, 𝑛1 Node, 𝑛2
Node, n
VNFC Features, 𝑓𝑛 Neighbourhood, , 𝑛∗
Using fn and n?, a state sn, and an output on for each node nare determined using equations (1) and (2) respectively.
sn =∑m∈n?
hw(fn, fm, sm
),∀n (1)
on = gw(sn, fn
), ∀n (2)
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
GNN-based Dynamic Resource Management
Features 𝑓𝑛of VNFC
Features 𝑓𝑚
of all VNFC’s Neighbours
ℎ𝑤 𝑔𝑤
VNFC State𝑠𝑛
Output(Resource Forecast)
FNN FNN
States 𝑠𝑚of all Neighbours
VNFC States
SFC Features
Output Computation
State Computation
3 4
1
2
Comprised of four main components: (1) SFC features, (2)VNFC states, (3) state computation, and (4) outputcomputation.
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
SFC Features
Observations or monitoring data from the VNFCsInclude network parameters (such as CPU or RAM utilisationlevels) that can be measured.
fn =
cnmn
dn
(3)fnm =
[bnmdnm
](4)
-SFC modelled as a directed graph G (N, L)
Virtualization container such as a
VM
VNFC 1
VNF 1 VNF 2
VNFC 1
VNFC 2 VNFC 3
VNF 4
VNFC 1
VNF 3
Service Function Chain based on Virtualised Network Functions
VNFC 3 VNFC 4
VNFC 2 VNFC 5
𝑛2
𝑛3 𝑛4
𝑛5
𝑙32
𝑙21
𝑙14
𝑙46𝑙31
𝑙15
𝑙51𝑛0
𝑛7
𝑛8
VNF 1
VNF 2
VNF 3
VNF 4
𝑛5
𝑙41
𝑛6𝑛0
𝑙41
𝑙4
𝑥4
𝑥1
𝑙1
𝑙14
𝑙31
𝑙3𝑥3
𝑙13
VNFC 𝑛1
Neighbourhood of VNFC 𝑛1
𝑛1
𝑙12
𝑙13𝑙23
𝑙03
VNFC 1
VNF 2𝑛2
VNFC State VNFC Features
𝑛3
𝑛1
𝑠3
𝑓3
𝑠2
𝑓2
𝑛4
𝑛5
𝑠1𝑓1
𝑠1 𝑓1
𝑠3𝑓3
𝑠2 𝑓2
𝑠4 𝑓4
𝑠1 𝑓1
𝑠1𝑓1
𝑠5𝑓5
𝑛1
𝑠1 𝑓1
ℎ𝑤
𝑔𝑤
o1
𝑛5𝑠5 𝑓5
𝑔𝑤
ℎ𝑤
𝑛4
𝑠4 𝑓4
ℎ𝑤
𝑔𝑤
o4
o5
𝑛2𝑠2 𝑓2
𝑔𝑤
ℎ𝑤
𝑛3
𝑠3 𝑓3
ℎ𝑤
𝑔𝑤
o3
o2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤𝑥2
𝑥3
𝑥1
𝑥5
𝑥4
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤. . .
. . .
. . .
. . .
. . .
𝑠5
𝑠1
𝑠3
𝑠2 o2
o3
o1
o5
o4
𝑖0 𝑖1 𝑖2 𝑖𝑇
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑠4
𝑓2
𝑓4
𝑓5
𝑓1
𝑓3
VNFC 1
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
VNF States
Virtualization container such as a
VM
VNFC 1
VNF 1 VNF 2
VNFC 1
VNFC 2 VNFC 3
VNF 4
VNFC 1
VNF 3
Service Function Chain based on Virtualised Network Functions
VNFC 3 VNFC 4
VNFC 2 VNFC 5
𝑛2
𝑛3 𝑛4
𝑛5
𝑙32
𝑙21
𝑙14
𝑙46𝑙31
𝑙15
𝑙51𝑛0
𝑛7
𝑛8
VNF 1
VNF 2
VNF 3
VNF 4
𝑛5
𝑙41
𝑛6𝑛0
𝑙41
𝑙4
𝑥4
𝑥1
𝑙1
𝑙14
𝑙31
𝑙3𝑥3
𝑙13
VNFC 𝑛1
Neighbourhood of VNFC 𝑛1
𝑛1
𝑙12
𝑙13𝑙23
𝑙03
VNFC 1
VNF 2𝑛2
VNFC State VNFC Features
𝑛3
𝑛1
𝑠3
𝑓3
𝑠2
𝑓2
𝑛4
𝑛5
𝑠1𝑓1
𝑠1 𝑓1
𝑠3𝑓3
𝑠2 𝑓2
𝑠4 𝑓4
𝑠1 𝑓1
𝑠1𝑓1
𝑠5𝑓5
𝑛1
𝑠1 𝑓1
ℎ𝑤
𝑔𝑤
o1
𝑛5𝑠5 𝑓5
𝑔𝑤
ℎ𝑤
𝑛4
𝑠4 𝑓4
ℎ𝑤
𝑔𝑤
o4
o5
𝑛2𝑠2 𝑓2
𝑔𝑤
ℎ𝑤
𝑛3
𝑠3 𝑓3
ℎ𝑤
𝑔𝑤
o3
o2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤𝑥2
𝑥3
𝑥1
𝑥5
𝑥4
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤. . .
. . .
. . .
. . .
. . .
𝑠5
𝑠1
𝑠3
𝑠2 o2
o3
o1
o5
o4
𝑖0 𝑖1 𝑖2 𝑖𝑇
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑔𝑤
𝑠4
𝑓2
𝑓4
𝑓5
𝑓1
𝑓3
VNFC 1
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤𝑠3
𝑠4
𝑠1
ℎ𝑤
ℎ𝑤𝑠2
𝑓2 𝑓3
𝑓1
𝑓4
ℎ𝑤𝑠5
𝑓5
𝑛4
𝑛3𝑛2
𝑛1
𝑛5
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
State Computation (1)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤𝑠3
𝑠4
𝑠1
ℎ𝑤
ℎ𝑤𝑠2
𝑓2 𝑓3
𝑓1
𝑓4
ℎ𝑤𝑠5
𝑓5
𝑛4
𝑛3𝑛2
𝑛1
𝑛5
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(1)
𝑠2(1)
𝑠1(1)
𝑠5(1)
𝑠4(1)
𝑠2(0)
𝑠1(0)
𝑠3(0)
𝑠1(0)
𝑠3(0)
𝑠1(0)
𝑠5(0)
𝑠4(0)
𝑠1(0)
𝑠1(0)
𝑓2 , 𝑓3
𝑓1 , 𝑓2
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
State Computation (2)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(2)
𝑠2(2)
𝑠1(2)
𝑠5(2)
𝑠4(2)
𝑠2(1)
𝑠1(1)
𝑠3(1)
𝑠1(1)
𝑠3(1)
𝑠2(1)
𝑠5(1)
𝑠4(1)
𝑠1(1)
𝑠1(1)
𝑓2 , 𝑓3
𝑓1 , 𝑓2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(1)
𝑠2(1)
𝑠1(1)
𝑠5(1)
𝑠4(1)
𝑠2(0)
𝑠1(0)
𝑠3(0)
𝑠1(0)
𝑠3(0)
𝑠2(0)
𝑠5(0)
𝑠4(0)
𝑠1(0)
𝑠1(0)
𝑓2 , 𝑓3
𝑓1 , 𝑓2
Iteration 1 Iteration 2
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
State Computation (3)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(1)
𝑠2(1)
𝑠1(1)
𝑠5(1)
𝑠4(1)
𝑠2(0)
𝑠1(0)
𝑠3(0)
𝑠1(0)
𝑠3(0)
𝑠2(0)
𝑠5(0)
𝑠4(0)
𝑠1(0)
𝑠1(0)
𝑓2 , 𝑓3
𝑓1 , 𝑓2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(2)
𝑠2(2)
𝑠1(2)
𝑠5(2)
𝑠4(2)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(3)
𝑠2(3)
𝑠1(3)
𝑠5(3)
𝑠4(3)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(𝑇)
𝑠2(𝑇)
𝑠1(𝑇)
𝑠5(𝑇)
𝑠4(𝑇)
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
Iteration 1 Iteration 3Iteration 2 Iteration T
State Computation
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Output computation
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(1)
𝑠2(1)
𝑠1(1)
𝑠5(1)
𝑠4(1)
𝑠2(0)
𝑠1(0)
𝑠3(0)
𝑠1(0)
𝑠3(0)
𝑠2(0)
𝑠5(0)
𝑠4(0)
𝑠1(0)
𝑠1(0)
𝑓2 , 𝑓3
𝑓1 , 𝑓2
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(2)
𝑠2(2)
𝑠1(2)
𝑠5(2)
𝑠4(2)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(3)
𝑠2(3)
𝑠1(3)
𝑠5(3)
𝑠4(3)
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
ℎ𝑤
𝑠3(𝑇)
𝑠2(𝑇)
𝑠1(𝑇)
𝑠5(𝑇)
𝑠4(𝑇)
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
𝑂5𝑔𝑤
𝑂4𝑔𝑤
𝑂1𝑔𝑤
𝑂2𝑔𝑤
𝑂3𝑔𝑤
Iteration 1 Iteration 3Iteration 2 Iteration T
State ComputationOutput Computation
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Summary
1
2
3
0
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Implementation Setup
Bono Sprout
Ralf Homer Homestead
HSS Mirror
cassandra
XDMS
cassandra
Rf CTF
memcached
I/S-CSCF BGCF
memcachedP-CSCF, WebRTC
Clearwater virtualised IMS
SNMPUEs
SIPp
GNN-based Dynamic Resource
ManagementDNS
Heat Orchestration
SIP
CACTIMonitoring
SUT
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Evaluation Details
Setup Parameters and Comparisons
1 100K Users, Call initiation/end based on Poisson/Exponential,
2 Each call transmits media extracted from real Skype traffictraces
3 All VNFCs polled every 15s, History/Forecasting is 20episodes,
4 Experiment 1: 10,000 data points for training FNNs
5 Experiment 2: Trained System used to determine accuracy on1,000 measurements
6 Experiment 3: Predictions used to effect resource allocations(Spin-up at 40%, Spin down at 20%)
7 Comparisons: Static, Manual, Automated
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Evaluations (1)
0
10
20
30
40
50
60
0 200 400 600 800 1000
RM
SE
Training Iteration, each involving 10,000 examples
Ralf Bono Sprout
Homestead Homer Total
0.00
0.20
0.40
0.60
0.80
1.00
0 200 400 600 800 1000
% C
PU U
tlis
atio
n
Test Number
Actual Output Expected Output
0.00
0.50
1.00
1.50
2.00
2.50
3.00
0 200 400 600 800 1000
Del
ay (
ms)
Test Number
Actual Output Expected Output
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000
% D
elay
Pre
dic
tio
n E
rro
r
Test Number
Error 100 period Mov. Avg.
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Evaluations (2)
0
0.2
0.4
0.6
0.8
1
100 400 700 1000
% C
PU U
tilis
atio
n
Test Number
Static Manual Automated
0.00
0.50
1.00
1.50
2.00
2.50
3.00
100 400 700 1000
Del
ay (m
s)
Test Number
Static Manual Automated
0
2
4
6
8
100 400 700 1000
Dro
pped
Cal
lsTh
ousa
nds
Test Number
Static Manual Automated
0
10
20
30
40
100 400 700 1000
Dro
pped
Cal
lsTh
ousa
nds
Test Number
Static Manual Automated
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
Conclusion and Future Work
Conclusion
Topology-aware approach to automated and dynamicresource management approach for NFV environments.
Implemented in a real environment involving a virtualisedIMS, and using real VoIP traces,
Prediction accuracy of about 90%, and enhance theprocessing delay and call drop rate by 29% and 27%respectively.
Future Work
Improve generalisation accuracy by considering error functionswith different penalty terms.
More efficient ways of training the SFC encoding network.
Introduction Problem Proposed Approach Solution Model Evaluation Conclusion
