A E l ti b d D iAn Evolutionary based Dynamic Energy Management Framework forEnergy Management Framework for

IP-over-DWDM Core Networks

Xin Chen, Chris PhillipsSchool of Electronic Engineering &

C t S iComputer Science

O tliOutline

Introductionk dBackground

New Energy Management DesignNew Energy Management DesignSimulationFurther work

2

IntroductionIntroduction

Benefits of Energy SavingEconomical

Lower OPEX for ISPsLower OPEX for ISPsEnvironmental

Lower CO2 emissions

3

d iIntroduction

Our energy management scheme combines gy ginfrastructure sleeping and virtual router migration together with automatic opticalmigration together with automatic optical layer connection forwarding to enable resources to be used in an energy efficientresources to be used in an energy-efficient manner.

4

k dBackground

Network ArchitectureNetwork Architecture Energy Saving ApproachesInfrastructure sleeping and Virtual Router Migration TechniquesRouter Migration Techniques

5

Network ArchitectureIP over DWDM

Network ArchitectureIP over DWDM

We apply the wavelength continuity constraint. There is no wavelength conversion for through-traffic in the network.

6

S i A hEnergy Saving Approaches

Static MechanismsStatic MechanismsNetwork planning, i.e. ILP

Dynamic Mechanismsf l i d iInfrastructure sleeping, rate adaptation,

network virtualization…..

7

Infrastructure Sleepingp gSwitch off unneeded equipment during off-peak periodsperiodsPrevious work [over 20% saving]

L. Chiaraviglio, M. Mellia, and F. Neri, "Energy-Aware Backbone Networks: A Case Study" in IEEE International Conference on Communications Workshops, 2009, pp. 1–5, June 2009.

8

f Sl iInfrastructure Sleeping

Some issues and limitationsSome issues and limitations

The problem of loss of connectivity due p yto reconvergenceWhen to sleep / wake ?When to sleep / wake ?How to sleep / wake ?

9

Virtual Router MigrationgMove virtual routers among difference physical platform

i h d di h iwithout degrading the service

Wang, Yi,; Keller, E.; Biskeborn, B.; Jacobus van der Merwe, Rexford, J.; ,"Virtual routers on the move: live router migration as a network-management primitive," SIGCOMM Comput. Commun. Rev. 38, 4 (August 2008), 231-242.

10

i l i iVirtual Router Migration

Some issues and limitations

When to trigger virtual router migration?When to trigger virtual router migration?Where to move virtual routers to?

11

Dynamic Energy ManagementDynamic Energy Management Framework

Overall Energy Management Procedure

Optical Connection Management

VRM_MOEAVirtual Router Migration – Multi-Objective Evolutionary Algorithm

12

Overall Energy Management ProcedureOverall Energy Management Procedure

1 C ll t d l th t k t t1. Collect and analyze the network status

2. Trigger VRM MOEA. (Quiet and Busy Thresholds)gg _ (Q y )

3. Establish the new optical connections

4. Virtual router migration5 Then Switch off (on) the corresponding physical5. Then Switch off (on) the corresponding physical

platforms and removed the unneeded optical connectionsconnections

6. Go back to step 1 to recheck the network status

13

Dynamic Optical Connection ManagementAdditi l ti l ti d d fAdditional optical connections are needed for forwarding the traffic to the remote virtual

t ( ) i th k trouter(s) processing the packets

Changes in the underlying physical network are hidden from the topology as seen by Layer-3 and p gy y yso reconvergence events are avoided

14

Example: Optical Connection Management

15

Destination Physical Platform Selection yAlgorithm- VRM_MOEA

Individual chromosome representation:

Gene VR index

Chromosome length set to sum of VRs

2Gene, VR index

Allele gives PP location211

16

Destination Physical Platform Selection yAlgorithm - VRM_MOEA

Initial Population: Pre screening procedure for selecting the variable solutions

Evolutionary algorithm well-suited to real-time operation as search can be halted at any point and we only need a “good”and we only need a good solution

17

Destination Physical Platform Selection yAlgorithm- VRM_MOEA

T bj ti f tiTwo objective functions: 1. Power Consumption:

α

1-total base i lc base roadm

iP P N P P P

α

α θ β α β=

= ⋅ + ⋅ + ⋅ ⋅ + ⋅∑ （ ）

On PPs On PP Linecards Off PPs On ROADMsto ta lP

P

αβ

----- The power consumption of the network

----- The number of active PPs----- The number of ROADM in the

On PPs On PP Linecards Off PPs On ROADMs

b a seP

lcP θ----- The power consumption of base system----- The power consumption of

network----- A percentage of the base system power consumption a PP consumes

h i i l iro a d mP

iNa line card----- The power consumption of a ROADM

when it is sleeping.----- The number of active line cards in the i-th PP

18

Destination Physical Platform Selection Al ith VRM MOEAAlgorithm- VRM_MOEA

2. Virtual Router Migration Cost (Second objective function)

The first VRM cost component comes from hop count f i i l “h ” VR l ti t it d ti ti PPfrom an original “home” VR location to its destination PP. For i-th candidate solution:

β

01

_ ( ) ( , )j ji

j

Cost a i d g gβ

=

= ∑

0ig

----- A function for obtaining the distance between two PPs: x1 and x2. ----- j-th gene in the default network configuration

( 1, 2 )d x x

jig

β

----- j-th gene in a candidate solution.

----- The number of VR

19

Destination Physical Platform Selection Al ith VRM MOEAAlgorithm- VRM_MOEA

The second component comes from the virtual routerThe second component comes from the virtual router migration process

β

1_ ( ) ( , )j j

current ij

Cost b i d g gβ

=

=∑ic u rren tg ----- j -th gene in the current network

Therefore The overall cost of one possible solution

c u rren tg

jig

j gconfiguration----- j-th gene in a candidate solution.

Therefore, The overall cost of one possible solution is :

( ) ( ) (1- ) ( )Cost i Cost a i Cost b iϕ ϕ= ⋅ + ⋅( ) _ ( ) ( ) _ ( )ϕ ϕ

ϕ ----- Weight of two cost terms

20

Destination Physical Platform Selection yAlgorithm- VRM_MOEA

Fi f iFitness function:

Strength Pareto Evolutionary Algorithm II (SPEA2)

Selection Mechanism:

To rn ment sele tionTournament selection

Crossover Operation:

BLX-α crossover

Mutation Operation:Mutation Operation:

Mutation rate = 0.1

21

Strength Pareto Evolutionary Algorithm II (SP A2)II (SPEA2)

The relationship between two decision vectors : Dominance , indifferenceThe relationship between two decision vectors : Dominance , indifference

22

Strength Pareto Evolutionary Algorithm II (SP A2)II (SPEA2)

SPEA2 P dSPEA2 Procedure:

1.Assign a strength score to each solution. The score is equal to the number of

solutions it dominates.

2.Get the raw fitness value of a solution by summing up the strength score of

solutions which dominate it.

3.Get the density value by K-th nearest neighbor method (K=1).

4.Add the raw fitness value and density value to obtain the fitness value. A non-

dominate solution has fitness value 0.

23

Strength Pareto Evolutionary Algorithm II (SP A2)II (SPEA2)

24

BLX-α CrossoverBLX α CrossoverIt offers an opportunity that after crossover, the offspring’s genes come pp y p g gfrom a slightly larger range randomly selected between the two parents’ genes.

Procedure:Procedure:For two Parets: G1, G2, the i-th gene of offspring is define:

[ ]h Uniform g I g Iα α= − ⋅ + ⋅

ih

min max[ , ]ih Uniform g I g Iα α= − ⋅ + ⋅1 2

min ( , )i ig Min g g=1 2 11 2

max ( , )i ig Max g g=

max minI g g= −

1ig2ig

----- i-th gene of G1----- i-th gene of G2----- An user define parameterα

25

Simulation

Simulator IntroductionSimulation Results

26

Simulator Introduction 1. Main simulation framework: Hybrid simulator

2. Network topology: a simple network topology generatorp gy p p gy g

3. Traffic model: fluid flow model and daily traffic model

4 D i i h i l l f l i bl4. Destination physical platform selection problem:

VRM_MOEA

5. When to trigger VRM : Reactive mechanism

27

Simulation Results Network 6N8L 11N14L

Scheme Name Energy Energy Energy Energy

Consumption / day Saving Consumption / day Saving

No VRM 4057200.00 0.00% 7698240.00 0.00%

Quick VRM 3223327.20 20.55% 6008048.00 21.96%

VRM_MOEA(0 ,1) 3134507.20 22.74% 5457832.20 29.10%

VRM_MOEA(0.2,0.8) 3125365.00 22.97% 5451220.20 29.97%

VRM_MOEA(0.5,0.5) 3140139.80 22.60% 5471056.80 28.93%

VRM_MOEA(0.8,0.2) 3160408.40 22.10% 5490892.40 28.67%

VRM_MOEA(1 ,0) 3144768.20 22.49% 5517340.00 28.33%

A l 5 i l ti ith d dAverage values over 5 simulations with random seeds

Quick VRM chooses the best solution in a randomly generated population of candidate solutions without any evolutionary process

28

y y

Simulation ResultsSimulation Results

The optical infrastructure

The energy saving is similar among the different VRM_MOEA schemes

is “always on”

In the off-peak hours, e.g. 12 to 24, the energy saving performance of VRM_MOEA is better than Quick VRM

29

Simulation Results

Per fibre, there are 40 channels each operating at ope a g a40Gb/s The PP switch fabric can accommodate 1Tb/s traffic load

The occupied number of lightpaths fluctuate with the traffic load in the baseline schemescheme.

When VRs are moved to remote PPs , more optical channels are used for transmitting the packets to be processed by VRs than that of the baseline case.

The discontinuities on the lines correspond to virtual router migrations.

30

The discontinuities on the lines correspond to virtual router migrations.

Simulation ResultsSimulation Results

Higher quiet and busy thresholds cause a higher energy saving.g q y g gy g

31

Simulation ResultsSimulation Results 60

1 γ⎡ ⎤

50

60

)

It is more difficult to gain

1-( ) (1 sin( ))2

ij ijT t f tγδ γ⎡ ⎤= Α ⋅ + ⋅ +⎢ ⎥⎣ ⎦40

30

y Sa

ving

(%)

gthe energy saving in a busier network.20

10

Ener

gy

10

32

Future WorkFuture Work

1. Reactive mechanism Short term proactive mechanism

2 Add the VRM migration time into the simulation2. Add the VRM migration time into the simulation

33

Thank you!Thank you!

34