Network Adaptability under Resource...

Network Adaptability under Resource CrunchRafael Braz Rebouças LourençoNetworks Lab - UC DavisFriday Lab Meeting - April 7th, 2017

Outline

• What is Resource Crunch• Problem Statement• Example 1• Connection Adjacency Graph (CAG)• Splitting the problem• Example 2• Algorithm• Results

2

Resource Crunch

• Different from occasional request blocking, consists of a situation in which offered demand cannot possibly be carried by the network• May be caused by:

1. Failure arrivals (disasters) ➔ decrease transmission capacity2. Traffic demand arrivals ➔ increase offered load

➔ How can we deal with Resource Crunch on layer 2.5 (MPLS/SDN flows)?

3

FOCUS: Resource Crunch due to Failures or Unexpected Traffic Surges (Flash Crowd)• Unpredicted (or underestimated)

spikes in the traffic

4

[1]Ali-Eldin,etal."Anadaptivehybridelasticitycontrollerforcloudinfrastructures." NetworkOperationsandManagementSymposium(NOMS),2012IEEE.[2]https://cloudplatform.googleblog.com/2016/09/bringing-Pokemon-GO-to-life-on-Google-Cloud.html[3]Tang,etal."Dynamicrequestredirectionandelasticservicescalingincloud-centricmedianetworks." IEEETransactionsonMultimedia 16.5(2014)

• In cloud services environments, is commonly dealt with by spreading computation and/or redirecting traffic

[2]Pokemon Go:PredictedXObservedtraffic

[1]RapiddemandchangefortheFIFAworldcupwebsite

[3]AverageYoutube trafficthroughout24h.Abnormalitiesbetweenredlines

But also… Long Term Traffic Growth

• Traffic continually grows, Cisco VNI estimates general internet traffic growth at an average of 22% a year for the next 5 years

• Network engineering activities are cyclically performed to install new network capacity and avoid bottlenecks in the system

• Several networks are already operating well above traditional occupancy levels (intra-datacenter networks, specially)

5

0

50

100

150

200

250

300

350

400

1 13 25 37 49 61 73 85 97 109

121

133

145

157

169

181

193

205

217

229

241

253

265

277

TotalBandw

idth(G

bps)

Weeks

NetworkCapacityandOffered/BlockedTrafficEvolution

Capacity Demand CarriedTraffic BlockedTraffic

10%5%

15%

40%peryear

Building Block: Flexible Service Level Objectives for different Service ClassesService Real

TimeDegradable Ratio of

alltrafficRequested

GbpsMinimumGbps

DegradableCapacity

Price perGbps perLink

BlockingCost

ControlTraffic(SCADA,etc)

Y N 10% 2 2 0 $5 $10

BigDataTransfer(Backups, etc)

N Y 20% 10 5 upto5 $2 $5

SmallDataTransfers

N Y 20% 5 4 upto1 $3 $5

VideoonDemand(Youtube Netflix)

N Y 30% 3 1 upto2 $1 $1

HD Real-TimeVideo(HDTV)

Y Y 14% 4 2 upto2 $2 $1

Non-HD Real-TimeVideo(RegularTV)

Y Y 6% 2 1 upto1 $1 $0

6

*DifferentSLOs(suchasDegradability,Capacity,orAvailability,Latency,andothersnotshownhere)yieldindifferentprices

Network Adaptability Under Resource Crunch

If an incoming demand cannot be placed due to Resource Crunch…

7

…can it somehow be served? (by degrading other already allocated demands)

If so, can we maximize the operator’s revenue while serving the demand?

➔ Attheexpenseofdegradingwhichotherdemands?

➔ Usingwhatthroughput?

➔ Throughwhichpath?

Problem Statement

• Given:• Network topology• Potentially flexible SLOs for different Service Classes• Set of currently allocated demands (respective paths, flexible SLOs, prices/priorities)• An incoming demand (and its SLO) that cannot be placed due to Resource Crunch

• Output:• A decision of whether or not to serve that demand, and if so, through what path, at what

throughput, and by degrading which other demands• Goal:

• Maximize the overall revenue of the network operator• Constraints:

• Link rates, SLOs, network topology

8

SimplifiedProblemStatement:IfanincomingdemandcannotbenormallyservedduetoResourceCrunch:whichotherconnectionsshouldwedegradeinordertoservethisdemand

(orshouldwenotserveitatall)?

Illustrative Example - 1

9

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

Allconnectionshavethesameminimumandmaximumrequiredthroughputs:Min(C1)=Min(C2)=Min(C3)=Min(C4)=Min(C5)=10GbpsMax(C1)=Max(C2)=Max(C3)=Max(C4)=Max(C5)=20Gbps

Thus,alllinkarebeingfullyutilized.

Connection4,Cost$4/Gbps




10

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

Anewrequestof10GbpsarrivesfromAtoFandcannotbenormallyservedduetoResourceCrunch.Thisnewrequestofferstopay$4/Gbps.




One Idea: Shortest path routing…

11

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

DegradeConnections5and4,andplacethenewrequestontheshortestpath.Thedegradationwoulddecreasetherevenuein$(5x10+4x10)andthenewrequestwouldincreaseitin$40.

Totalrevenuedecreasedby$50.




One Idea: Shortest path routing on prices…

12

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

Doesnotwork➔ Onceyoupaythefirstcost,thefollowingedgesoftheconnectionshouldbe”free”…




So,shortest-pathroutingapproachesmightnotbebestoptioninaResourceCrunchscenario…

However…

13

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

Noticehoweverytimeaconnectionisdegradeditfrees-upcapacitythroughoutitsentirepath…Wouldn’ttherebeamoreefficientwaytoutilizethecapacitythatwasliberated?




Another Idea: Connection Adjacency Graph (CAG)

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Connection1[A,B,D,E]

Connection2[B,C,D,F]

Connection3[D,E,F]

Connection4[G,F,E]

Connection5[A,G,B]

First,createsuper-verticesrepresentingeachconnection.Annotateineachsuper-vertexthesetofphysicalnodesthatconnectiontouches.

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Connection1[A,B,D]


Connection3[D,E,F]

Connection4[G,F,E]

Connection5[A,G,B]

Then,addedgesconnectingeachsuper-vertextoallothersuper-verticesthatcontainatleastonephysicalnodeincommon.Add2edges,oneineachdirection.



Connection1[A,B,D]


Connection3[D,E,F]

Connection4[G,F,E]

Connection5[A,G,B]

Addtwodummynodes.Dummy-SourcerepresentingthesourceofthenewrequestandDummy-Target,forthetarget.Connecteachofthemtoallsuper-verticesthatcontaineitherthesourceorthetarget.

Dummy-Source[A]

Dummy-Target[F]

Connection1[A,B,D]


Connection3[D,E,F]

Connection4[G,F,E]

Connection5[A,G,B]

Associatewitheachedgeacost(aka,weight).Foranyedge,exceptthoseincomingtotheDummy-Target(whosecostareall0),thecostofthisedgeisthecostofdegradinginonethroughput-unittheconnectionthesuper-vertexthatedgepointstorepresents.

Dummy-Source[A]

Dummy-Target[F]






Cost=5

Cost=5

Cost=2

Cost=3

Cost=2

Cost=0*Or,possibly,thecostperbandwidthunitdividedbythenumberoflinksutilizedbytheconnection,oramortizedbytheremainingdurationoftheconnection…


Connection1[A,B,D]


Connection3[D,E,F]

Connection4[G,F,E]

Connection5[A,G,B]

Finally,calculatetheshortest(cheapest)pathfromDummy-SourcetoDummy-Target.Inthiscase,becausethetotalcostoftheshortestpathislessthenthepricethenewrequestisofferingtopay,degradeConnection1andConnection2andallocatethenewrequest…

Dummy-Source[A]

Dummy-Target[F]

Cost=5

Cost=5

Cost=2

Cost=3

Cost=2

Cost=0Shortestpath.Totalcost=3.

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20

DegradeConnection1andConnection2totheirminimum(i.e.,10Gbps)….




20

E

D

G

B

A

F

C

CAP=10

CAP=10

CAP=0

CAP=0

Theresidualgraphaftercapacityisfreed-upduetothedegradationofConnections1and2.

21

E

D

G

B

A

F

C

CAP=10

CAP=10

CAP=0

CAP=0

Now,twopaths(of10Gbpscapacity)areavailable.Choosetheshortestone.

E

D

G

B

A

F

C

CAP=20

CAP=20

CAP=20

CAP=20


Connection2(DEGRADED– 10Gbps),Cost$2/Gbps

Totalrevenue➔ - (10x1)- (10x2)+(10x4)➔ Requestnotblockedandrevenueincreasedin$10

Connection1(DEGRADED– 10Gbps),Cost$1/Gbps

NEWConnection-10Gbps,Cost$4/Gbps

Proof of Correctness

• CAG contains all possible combinations of connection degradations that one can perform in any feasible path through the network • CAG precisely describes the cost of degrading a connection (since edges

weights are the costs of degrading their target super-vertex connection)• There is a one-to-one mapping from any path in the physical network to a

path in the CAG. There is a one-to-many relationship between one path in the CAG to many paths in the physical network

ØThus, a shortest (cheapest) S-T path in the CAG necessarily maps to a cheapest set of connection degradations that create room for a path to be routed from source to destination

23

However…• Note that in the example, the purple request only asked for 10 Gbps• Note how every initially allocated connection had a degradable capacity of 10 Gbps

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SimplifiedProblemStatement:IfanincomingdemandcannotbenormallyservedduetoResourceCrunch:whichotherconnectionsshouldwedegradeinordertoservethisdemand

(orshouldwenotserveitatall)?

➔ (…)giventhattheincomingdemandfitsineachandeverydegradablecapacityofeachalreadyallocatedconnection?

➔ (…)giventhattheincomingdemanddoesnotnecessarilyfitineachandeverydegradable

capacityofeachalreadyallocatedconnection?

P NotinP


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E

D

G

B

A

F

C

CAP=40

CAP=40

CAP=40

CAP=40



AllLinksnowhave40Gbpscapacities.Allconnectionshavethesameminimumandmaximumrequiredthroughputs:Min(C1)=Min(C2)=Min(C3)=Min(C4)=Min(C5)=Min(C6)=10GbpsMax(C1)=Max(C2)=Max(C3)=Max(C4)=Max(C5)=Max(C6)=20Gbps





26

E

D

G

B

A

F

C

CAP=40

CAP=40

CAP=40

CAP=40



Anewrequestof20GbpsarrivesfromAtoFandcannotbenormallyservedduetoResourceCrunch.Thisnewrequestofferstopay$11/Gbps.





27

E

D

G

B

A

F

C

CAP=40

CAP=40

CAP=40

CAP=40






Oneoption:DegradeConnections1,2and7.Thedegradationwoulddecreasetherevenuein $(1x10+2x10+10x10)=$130andthenewrequestwouldincreaseitin$110.

Totalrevenuedecreasedby$20.


28

E

D

G

B

A

F

C

CAP=40

CAP=40

CAP=40

CAP=40






Anotheroption:DegradeConnections4,5,and6.Thedegradationwoulddecreasetherevenuein $(5x10+6x10+4x10)=$100andthenewrequestwouldincreaseitin$110.

Totalrevenueincreasedin$10.

Demands that don’t fit in every single Degradable Capacity• Splittable demand ➔ find CAG cheapest path, allocate (same

procedure as before), repeat…• Non-splittable demand ➔ since the CAG contains all possible

degradations, the solution to this problem can be found within one of the possible combinations of CAG paths

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ØFinding all simple paths in a graph: NP hardØAll combinations: Exponential

Dealing with Intractability

1. Instead of calculating all CAG paths➔ only calculate the ”K-cheapest”

2. Instead of calculating all combinations➔ (with the help of a bipartite Degradation Oriented Graph…) only analyze the ”K- shortest” combinations of paths that share physical links

3. If no solution is found ➔ use the CAG paths of #1 to guide the search of a cheap (non-optimum) degradation

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Algorithm

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1.FindcheapestS-TpathintheCAG

Resourcecrunchnot-servedS-Tdemand(SLOs,price)

Demandfitafter

degradation?

2.FindKcheapestS-TpathintheCAG

3.FindKcheapestCAGpathcombinations

Demandfitafter

degradation?

4.UseKpathsfrom2toinducefreespaceinpaths

5.FindcheapestpossibleoptionamongtheK

Demandfitafter

degradation?

Isdegradationprofitable?



N N

YY Y

Y Y Y

Degradeotherconnectionsandallocatethedemand

PotentiallyOptimum?(DependingonK) Non-optimumHeuristic

*KeeptheCAGupdatedinmemory

Simulation• Use traffic mixture from the previous table (slide 6)• Statically occupy network up to average link utilization = 60%• Generate X random demands that would otherwise be blocked• Compare with a greedy approach [based on 1 and Journal submission]

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FindK=5shortestpathsinthephysicalnetwork

Resourcecrunchnot-servedS-Tdemand(SLOs,price)

Degradefromthecheapesttothemostexpensiveconnectionsinpath

Demandfitafter

degradation?


Allocate

Y

Trynext

Y

NN

[1]Roy,Abhishek,M.FarhanHabib,andBiswanath Mukherjee."Networkadaptabilityunderresourcecrunch." AdvancedNetworksandTelecommuncations Systems(ANTS),2014IEEEInternationalConferenceon.IEEE,2014.

Results

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00.050.10.150.20.250.30.350.40.45

30 60 90 120 150 180

DemandsInitiallyHinderedByResourceCrunch

IncreaseoverInitialRevenue(%)

Greedy CAG

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

30 60 90 120 150 180


RevenueIncreaseMinusBlockingCosts(%)

Greedy CAG0

0.01

0.02

0.03

0.04

0.05

30 60 90 120 150 180


TotalBlockingCostoverInitialRevenue(%)

Greedy CAG

47%

55%

61%

Results – Cont.

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0.58

0.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

0.76

0.78

30 60 90 120 150 180


AverageLinkUtilizationAfterExecution(%)

Greedy CAG

0

1

2

3

4

5

6

7

30 60 90 120 150 180


AverageLengthOfPaths(#oflinks)

Greedy CAG

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

30 60 90 120 150 180


SuccessfullyAllocated(afterdegrading)(%)

Greedy AllCAG CAG(OptimallyAllocated?)

38%5%

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