Load Sharing in Networking Systemsicapeople.epfl.ch/lkencl/lecture_files/kencl05loadsharing.pdf ·...

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Load Sharing in Networking Systems

Lukas Kencl Lukas Kencl Intel Research CambridgeIntel Research CambridgeMFF UK MFF UK PrahaPraha, November 2005 , November 2005

(With lots of help from Weiguang Shi, University of Alberta, and citations from D. Thaler, R. Ravishankar and K. Ross)

2www.intel.com/research

• Intel Research Cambridge •

OutlineOutlineIntel Research CambridgeIntel Research CambridgeLoad sharing in networking systemsLoad sharing in networking systems

Problem statementProblem statementMotivationMotivationImbalance due to traffic propertiesImbalance due to traffic properties

……………………………………. . Break Break ……………………………………………………………………..…………Inspiration: distributed Web cachingInspiration: distributed Web cachingSolutions and properties: Solutions and properties:

Adaptive HRW hashingAdaptive HRW hashingAdaptive Burst ShiftingAdaptive Burst Shifting

……………………………………. . Break Break ……………………………………………………………………..…………

Seminar: Seminar: Q&A, Exercises and Q&A, Exercises and HomeworksHomeworksIR Movies!!!IR Movies!!!



Intel Research CambridgeIntel Research Cambridge



IllinoisIllinois

Washington Washington

Oregon Oregon

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TsukubaTsukubaTokyoTokyo

DelhiDelhiBangaloreBangaloreMumbai Mumbai

IndiaIndia

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China

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NizhnyNizhnyNovgorodNovgorod

Sarov Sarov

Braunschweig,Braunschweig,UlmUlm

GdanskGdansk

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CambridgeCambridge

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NiceNice

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Over 75 labs & 7000 Over 75 labs & 7000 R&D professionals R&D professionals

worldwideworldwide

Intel R&D Commitment Intel R&D Commitment

ShannonShannon

MassachusettsMassachusetts

PennsylvaniaPennsylvania

Intel Research: 4 Intel Research: 4 LabletsLablets, target 80 people worldwide, target 80 people worldwide



Intel Research MissionIntel Research Mission

Build the technical leadership, knowledge assets Build the technical leadership, knowledge assets and systems perspective to make Intel a preeminent and systems perspective to make Intel a preeminent driver of disruptive information technologiesdriver of disruptive information technologies

David TennenhouseDavid TennenhouseVice President, Corporate Technology GroupVice President, Corporate Technology Group

Director of Research, Intel CorporationDirector of Research, Intel Corporation



What is unique about Intel Research?What is unique about Intel Research?

Research is largely exploratory Research is largely exploratory

Off roadmap, 7Off roadmap, 7--15 years out on timeline15 years out on timeline

Innovative collaborative model engaging with key UniversitiesInnovative collaborative model engaging with key Universities

““LabletsLablets”” (Berkeley, Seattle, Pittsburgh, Cambridge) (Berkeley, Seattle, Pittsburgh, Cambridge) –– Intel employee Intel employee facilities cofacilities co--located on University campus to facilitate collaborationlocated on University campus to facilitate collaboration

Open Collaborative Agreements signed with UniversitiesOpen Collaborative Agreements signed with Universities

Strategic Research Projects (SRP) inside Intel Strategic Research Projects (SRP) inside Intel

Technology transfer mechanism from open collaborative research Technology transfer mechanism from open collaborative research

Proprietary exploratory research outside scope of any business uProprietary exploratory research outside scope of any business unitnit

Alignment of University engagementsAlignment of University engagements

Research Council, Academic Relations, Labs, Visiting Faculty, InResearch Council, Academic Relations, Labs, Visiting Faculty, Internships!!! ternships!!!



Intel Research Cambridge (IRC)Intel Research Cambridge (IRC)

Established in March 2003Established in March 2003

Currently 12 fullCurrently 12 full--time researcherstime researchers

Visiting faculty, student internsVisiting faculty, student interns

Make sure the smartest work with usMake sure the smartest work with us:: cooperation with cooperation with Cambridge University, as well as others throughout UK, Cambridge University, as well as others throughout UK, Europe and elsewhereEurope and elsewhere



IRC Key Research Areas IRC Key Research Areas Optical Packet Switching Optical Packet Switching Virtualisation Virtualisation –– XenXen

ParaPara--virtualisation virtualisation -- run modified or new OS on specialised guest run modified or new OS on specialised guest architecturearchitecture

Wired NetworkingWired NetworkingCoMoCoMo –– Continuous MonitoringContinuous MonitoringAdaptive methodsAdaptive methods

Anticipate the wirelessAnticipate the wirelessWIP: from internet to WIP: from internet to IntairnetIntairnet

wwireless access network using multidirectional antennasireless access network using multidirectional antennas

HAGGLE: mobile adHAGGLE: mobile ad--hoc networkshoc networks

Ubiquitous computingUbiquitous computing



Load Sharing in Networking Systems Load Sharing in Networking Systems

Problem StatementProblem Statement



Map packets to processorsMap packets to processors

Assumptions:Assumptions:•• Data arrives in packets.Data arrives in packets.•• Any processor can Any processor can process any packet.process any packet.HeterogenousHeterogenous

processor capacityprocessor capacity μμjj..

Incoming PacketsIncoming PacketsIncoming PacketsIncoming Packets

Multiple (M)Multiple (M)ProcessorsProcessors

11

22

33

MM

Multiple (N)Multiple (N)InputsInputs

11

22

33

44

NN

Task:Task:•• Map packets to processors so that load within some Map packets to processors so that load within some measure of balance.measure of balance.



Packet flows: avoid remapping or out-of-sequence!!!Packet flows: avoid remapping or out-of-sequence!!!

Assumptions:Assumptions:•• Data arrives in Data arrives in packetizedpacketized flows.flows.

•• Any processor can Any processor can process any packet.process any packet.HeterogenousHeterogenous

processor capacityprocessor capacity μμjj..

Incoming PacketsIncoming PacketsIncoming PacketsIncoming Packets

Multiple (M)Multiple (M)ProcessorsProcessors

11

22

33

MM


11

22

33

44

NN

Task:Task:•• Load on processors within some measure of balance.Load on processors within some measure of balance.•• Same flow to same processor (reordering, context).Same flow to same processor (reordering, context).

Advantage:Advantage: system optimization.system optimization. Drawback:Drawback: complexity, overhead.complexity, overhead.



Reduce state maintenance of Reduce state maintenance of FlowFlow--toto--Processor Mapping Processor Mapping


11

22

33

NN

Multiple (M) Multiple (M) ProcessorsProcessors

11

22

33

44

MM

Incoming PacketIncoming Packet

flow flow identifier identifier vectorvector vv

v v

Upon packet arrival, a decision is made where to process the pacUpon packet arrival, a decision is made where to process the packet, based ket, based on the flow identifier. A on the flow identifier. A flowflow--toto--processor mapping processor mapping ff is thus established.is thus established.

f ( f ( vv ) = ) = 33



Acceptable load sharing Acceptable load sharing as a measure of balanceas a measure of balance

Processing load on processor Processing load on processor jj jj (t)(t)Capacity on processor Capacity on processor jj jj

Workload intensity on processor Workload intensity on processor jj jj(t(t)) = = jj(t(t)) / / jj

Total system workload intensityTotal system workload intensity (t(t)) = = Σ Σ jj(t(t)) / / Σ Σ jj

““No single processorNo single processor is is overutilizedoverutilized if the if the system in totalsystem in total is not overutilized,is not overutilized,and vice versa.and vice versa.””

Acceptable load sharing:Acceptable load sharing:ifif (t(t)) [ [ 1 1 thenthen ∀∀jj, , jj(t(t)) [ [ 1,1,if if (t(t)) >> 11 then then ∀∀jj, , jj(t(t)) >> 1.1.



Coefficient of Variation (CV)as a measure of balance

Coefficient of Variation (CV)as a measure of balance

Useful, as it takes the scale of measurements out of consideration.



Minimizing Disruption Minimizing Disruption Possible Goal:Possible Goal: Acceptable load sharing Acceptable load sharing withoutwithout maintaining maintaining flow stateflow state

information and yet information and yet minimizingminimizing the probability of the probability of mapping disruptionmapping disruption (flow (flow remapping or reordering).remapping or reordering).

Special 0Special 0--1 Integer Programming Problem:1 Integer Programming Problem:

maxmax ΣΣvv Δ Δ vv(t(t) . ) . ΣΣjj (1{ (1{ f(tf(t--Δ Δ t)(vt)(v) = j} .) = j} . 1{ 1{ f(t)(vf(t)(v)= j})= j}),),whilewhile ΣΣvv aavv(t(t) .) . 1{ 1{ f(t)(vf(t)(v)= j})= j} = = jj (t)(t) [ [ jj , , ∀ ∀ j.j.

v v -- flow identifier vector in the packet header,flow identifier vector in the packet header,ff(t)(t)(v(v)) -- function mapping flows to processors, changing over time,function mapping flows to processors, changing over time,ΔΔvv(t)(t)cc {0,1}{0,1} -- indicator ifindicator if vv has appeared in the intervalshas appeared in the intervals (t(t--22ΔΔ t, tt, t--ΔΔ t) t) and and (t(t--ΔΔ t, t)t, t),,aavv(t(t)) -- how many times hashow many times has vv appeared in the intervalappeared in the interval (t(t--ΔΔ t, t),t, t),

tt -- iteration intervaliteration interval. .

We suspect an We suspect an NPNP--complete problem complete problem –– use use heuristicsheuristics..



SummarizeSummarize

Balance loadBalance load

Avoid remapping Avoid remapping -- or packets outor packets out--ofof--sequencesequence

Minimize overheadMinimize overhead




MotivationMotivation



Practical ExamplesPractical ExamplesDistributed Router, or Parallel Forwarding EngineDistributed Router, or Parallel Forwarding Engine

Server Farm Load BalancerServer Farm Load Balancer

Network ProcessorNetwork Processor

Server Farm Load Balancer

S1

SM

S2

Packet-to-server

mapping computation

Incoming Packet

Packet forwarding

Multiple (M) Servers



Network ProcessorNetwork ProcessorNPNP

Pool of Microengines (ME)Pool of Microengines (ME)

GPPGPPSRAMSRAM DRAMDRAM

PacketsPackets

Network processor:Network processor:Pool of multiPool of multi--threaded threaded forwarding enginesforwarding enginesIntegrated or attached GPPIntegrated or attached GPPMemories of varying Memories of varying bandwidth and capacity bandwidth and capacity

Large parallel programming Large parallel programming problemproblem

ConcurrencyConcurrencyPacket orderPacket orderResource allocationResource allocationLoad balancingLoad balancing

At 10 At 10 GbpsGbps, packets arrive , packets arrive every 36 ns!!!every 36 ns!!!




Traffic properties make balancing difficultTraffic properties make balancing difficult



Traffic PropertiesTraffic Properties

Address distribution Address distribution –– 32bit address space very 32bit address space very unevenly assigned and populatedunevenly assigned and populated

BurstinessBurstiness

Power lawPower law



TCP Traffic is BurstyTCP Traffic is Bursty

TCP behaviour: ideal and typical

Packets arrive in periodic bursts!



Networking Traffic Exhibits Power-Law (Zipf-law) Properties

Networking Traffic Exhibits Power-Law (Zipf-law) Properties

Flow popularities in traffic traces

P(R)~1/Ra

Frequency of an event as a function of its rank obeys power-law. Popularity of network flows with aclose to 1 (from above).



Power-law leads to imbalance if static mapping (Shi et al, 2004)Power-law leads to imbalance if static mapping (Shi et al, 2004)m - number of processors,K - number of distinct object addresses or flow IDs,pi (0<i<K) - popularity of object i, qj (0<j<m) - number of distinct adresses or flow IDs mapped to processor j



Conclusion Part IConclusion Part I

Complex problemComplex problem

Traffic properties work against static solutionsTraffic properties work against static solutions



Break 5 minutesBreak 5 minutes




Inspiration: Distributed Web Caching and Web Inspiration: Distributed Web Caching and Web Server Load BalancingServer Load Balancing



What is a Distributed Web CacheWhat is a Distributed Web Cache

Intranet

Internet

Proxy Cache stores Web objects locally

Collection of distributed Web caches



HRW MappingHRW Mapping

Def.Def.: : ObjectObject--toto--Processor mapping functionProcessor mapping function f, f (v): Vf, f (v): V d d M :M :f (f (vv) = j) = j ww xxjj . g (. g ( v, v, jj ) = ) = maxmaxkk xxkk . g . g ((v, v, kk),),

wherewhere vv is the object identifier vector,is the object identifier vector, x x = (x= (x11, ... , x, ... , xmm)) is a weights' vector is a weights' vector and and g (g (vv, j), j)cc (0,1)(0,1) is a pseudorandom function of uniform distribution. is a pseudorandom function of uniform distribution.

Highest Random Weight (HRW) MappingHighest Random Weight (HRW) Mapping, , ThalerThaler, , RavishankarRavishankar, 1997, Ross, 1998, 1997, Ross, 1998, CARP Protocol, Windows NT LB, CARP Protocol, Windows NT LB

Minimal disruption of mappingMinimal disruption of mapping in case of processor addition or removal/failure.in case of processor addition or removal/failure.

Load balancing over Load balancing over heterogenousheterogenous processorsprocessors: weights: weights’’ vectorvector xx is in a 1is in a 1--toto--1 1 correspondence tocorrespondence to p p = (p= (p11, ... , p, ... , pmm)),, the vector of object fractions received the vector of object fractions received at each processor.at each processor.Pseudorandom function Pseudorandom function g (g (vv, , j)j)cc (0,1)(0,1) can be implemented as a fastcan be implemented as a fast--computable computable

hash function.hash function.

0

x1

x2

1 2 3

x2 . g (v, 2)

x1 . g (v, 1)

Map to max of:

x3 x3 . g (v, 3)Example: 3 processors



HRW Minimal DisruptionHRW Minimal Disruption

Example: Add processor No. 4, vectors mapped either (i) as before addition or (ii) to the newly added processor – minimal number of vectors change mapping.

0 1 2 3

g (v, 3)g (v, 1)

Max of:g (v, 2)

1

4

g (v, 4)

0 1 2 3

g (v, 3)g (v, 1)

g (v, 4)

4

g (v, 2)

1Max of:

Partitioning into contiguous set HRW

Minimal disruption of mapping in case of processor addition:



HRW Load BalancingHRW Load Balancingm - number of servers, S1, …, SmK - number of distinct object addresses or flow IDs, from the set of objects Opi (0<i<K) - popularity of object i, qj (0<j<m) - number of distinct adresses or flow IDs mapped to processor j , e.g. sum of the popularities of objects mapped to Sj




Adaptive MethodsAdaptive Methods-- Adaptive HRW HashingAdaptive HRW Hashing-- Adaptive Burst ShiftingAdaptive Burst Shifting



Adaptive HRW Hashing(Kencl, Le Boudec 2002)Adaptive HRW Hashing(Kencl, Le Boudec 2002)



Adaptive HRW mappingAdaptive HRW mapping



4. Download new4. Download newx := xx := x(t).(t).

2. Evaluate 2. Evaluate (t(t)) = (= ( 1 1 (t)(t), , 2 2 (t)(t), ... , , ... , mm (t)(t)))

(compare threshold).(compare threshold).

3. Compute new3. Compute newx x (t)(t) = (x= (x1 1 (t)(t), ..., , ..., xxmm (t)(t)).).

Adaptation through FeedbackAdaptation through Feedback

Trigger definition targets Trigger definition targets preventing overload, preventing overload, if system in if system in total not overloaded, and vice total not overloaded, and vice versa.versa.A A threshold threshold triggers adaptation triggers adaptation

when close to load sharing when close to load sharing bounds. bounds. FlowFlow--toto--processor mapping processor mapping ff

becomes a becomes a function of timefunction of timef(t)(f(t)(vv))Adaptation may cause Adaptation may cause flowflow

remapping! remapping! How to minimize the How to minimize the amount remapped?amount remapped?

Multiple (N)Multiple (N)Input CardsInput Cards

11

22

33

NN

Multiple (M)Multiple (M)processorsprocessors

11

22

33

44

MM

CPCP

Control PointControl Point

1. Filtered workload 1. Filtered workload intensity ( j) =intensity ( j) = jj(t).(t).

Problem:Problem: incoming requests incoming requests are packets, not flows! Packets not evenly distributed over floware packets, not flows! Packets not evenly distributed over flows s -->> not evenly distributed over the request object spacenot evenly distributed over the request object space --> HRW mapping not sufficient for > HRW mapping not sufficient for acceptable load sharing boundsacceptable load sharing bounds ––> need to> need to adapt!adapt!



Adaptation AlgorithmAdaptation AlgorithmStartStart

TriggerTriggerAdaptationAdaptation

??

Wait time Wait time tt

Adapt Adapt weights' vector weights' vector xx

and uploadand upload

Triggering PolicyTriggering Policy Adaptation PolicyAdaptation Policy

YesYesNoNo

Compute filtered Compute filtered processor workload processor workload

intensity intensity (t(t))



TriggeringTriggering PolicyPolicy

Dynamic workload intensityDynamic workload intensitythresholdthreshold

'' (t) = 1/2 (1+(t) = 1/2 (1+ (t))(t))

Triggering policyTriggering policy(i)(i) if if (t(t) ) [[ 1 1 andand maxmax jj (t) > (t) > (t) (t)

then then adaptadapt;;(ii) if(ii) if (t(t) > 1) > 1 andand minmin jj (t)(t) < < (t) (t)

then then adapt.adapt.

ExampleExample::(t) = (0.8, 0.2, 0.2), (t) = (0.8, 0.2, 0.2), (t) = 0.4(t) = 0.4

ee (t) = 0.7,(t) = 0.7,11 (t)(t) >> (t)(t) e e adapt.adapt.

0 10 20 30 40 50 60 70 80 90 1000.7

0.8

0.9

1

1.1

1.2

1.3

1.4

ρ(t), system in totalworkload intensity threshold

Triggering thresholdTriggering threshold

(t)(t) = = maxmax(( '' (t), upper)(t), upper)or vice versaor vice versa

0 10 20 30 40 50 60 70 80 90 1000.7

0.8

0.9

1

1.1

1.2

1.3

1.4

ρ(t), system in totaltriggering threshold

HysteresisHysteresis boundbound

upper: (1+upper: (1+ HH(t(t)) .)) . (t)(t)lower: (1 lower: (1 -- HH(t)) .(t)) . (t)(t)

0 10 20 30 40 50 60 70 80 90 1000.7

0.8

0.9

1

1.1

1.2

1.3

1.4

ρ(t), system in totalmax, min hysteresis bound



Adaptation Policy:Adaptation Policy:Minimal DisruptionMinimal Disruption

•• A, B A, B -- mutually exclusive subsets ofmutually exclusive subsets of M={1,...,m}M={1,...,m}, , M=AM=A 44 B.B.•• α α c c (0, 1).(0, 1).•• f, f'f, f' –– two HRW mappings with the weights' vectorstwo HRW mappings with the weights' vectors xx, , x'x'::

x'x'jj = = αα . . xxjj,, ∀ ∀ jj c c A,A,x'x'jj = = xxjj,, ∀ ∀ jj c c BB..

•• ppjj , , p'p'jj -- fraction of objects mapped to nodefraction of objects mapped to node j j usingusing ff, , f'f'..

1) 1) p'p'jj [ [ ppjj , , jj c c AA,,p'p'jj m m ppjj , j, j c c BB..

2) Fraction of objects mapped to a different node by each mapp2) Fraction of objects mapped to a different node by each mappingingisis MINIMAL, MINIMAL, that is, equal tothat is, equal to | | p'p'jj -- ppjj | . |V | | . |V | at every nodeat every node j. j.



• reduced receives less, unaltered receives more, if reduction by a single, invariable multiplier.• minimal disruption of the mapping.

0

x1

x2

1 2 3

x2 . g (v, 2)

x1 . g (v, 1)Max of:

1 2 3

x3

02/3.x1

2/3.x2

x3 . g (v, 3)

2/3 . x2 . g (v, 2)

2/3 . x1 . g (v, 1)Max of:

x3 x3 . g (v, 3)

Adaptation Policy: Minimal Adaptation Policy: Minimal Disruption Example (3 proc.)Disruption Example (3 proc.)



Adaptation PolicyAdaptation PolicyLet Let (t(t)) [ [ 11.. Then:Then:

xxjj (t) : = (t) : = c(t)c(t) . x. xj j (t(t-- t) ,t) , ifif jj (t)(t) >> (t) (t) (( jj exceeds thresholdexceeds threshold (t(t)))),,xxjj (t) : = (t) : = xxjj (t(t-- t),t), ifif jj (t)(t) [[ (t) (t) (( jj does not exceed thresholddoes not exceed threshold (t(t))))..

IfIf (t) > 1(t) > 1,, the adaptation is carried out in a symmetrical manner.the adaptation is carried out in a symmetrical manner.

The weights' The weights' multiplier coefficient multiplier coefficient c(tc(t)) ::

( )( )(t)(t)minmin {{ jj(t(t)) | | jj(t(t)) >> (t)(t)}}c(t) =c(t) =

1/m1/m

FactorFactor c(t)c(t) is proportional to the minimal error and to the number of nodes.is proportional to the minimal error and to the number of nodes.



Adaptive Burst Shifting(Shi, MacGregor, Gburzynski 2005)Adaptive Burst Shifting(Shi, MacGregor, Gburzynski 2005)



Adaptive Burst ShiftingAdaptive Burst Shifting

Insight: the periodicity of bursts may allow shifting flows in-between bursts, without affecting order within flows.



Adaptive Burst ShiftingAdaptive Burst Shifting

Burst Distributor Algorithm



Performance evaluationPerformance evaluation



Adaptive HRW on generated trafficAdaptive HRW on generated traffic



ExpectationsExpectations

•• Workload intensity on individual processors close Workload intensity on individual processors close to that of the system in total to that of the system in total (acceptable load (acceptable load sharing);sharing);•• Packet loss probability lowered Packet loss probability lowered (acceptable load (acceptable load sharing);sharing);•• Persistent flows Persistent flows (appearing in two consecutive iterations)(appearing in two consecutive iterations)seldom remapped seldom remapped (minimize disruption).(minimize disruption).



AHH Keeps Per-processor Workload Intensity Close to IdealAHH Keeps Per-processor Workload Intensity Close to Ideal

0 50 100 150 2000

0.5

1

1.5

2

2.5

Wor

kload

inte

nsity

Time

ρ(t), system in total

Naive, no LS.Naive, no LS.

0 50 100 150 2000

0.5

1

1.5

2

2.5

Wor

kload

inte

nsity

Time


Static HRW Static HRW

0 50 100 150 2000

0.5

1

1.5

2

2.5

Wor

kload

inte

nsity

Time


Adaptive HRWAdaptive HRW

0 50 100 150 2000

0.5

1

1.5

2

2.5

Wor

kload

inte

nsity

Time

ρ(t), system in totalMax, min ρj(t), no LSMax, min ρj(t), static LSMax, min ρj(t), adaptive LS

Max and min of all.Max and min of all.



Packet Loss Significantly Reducedwith the Adaptive Control LoopPacket Loss Significantly Reducedwith the Adaptive Control Loop

0 50 100 150 2000

2

4

6

8

10

x 104

Pac

kets

dro

pped

s

Time

System in totalNo LSStatic LSAdaptive LS

Packet loss: Naive, Static, Adaptive, Ideal. Packet loss: Naive, Static, Adaptive, Ideal.

0 50 100 150 2000

0.5

1

1.5

2

2.5

3

3.5x 10

4

Pac

kets

dro

pped

Time

Static LSAdaptive LS

Packet loss in excess of Ideal: Static, Adaptive.Packet loss in excess of Ideal: Static, Adaptive.

Adaptive HRW saves on average 60% Adaptive HRW saves on average 60% of packets dropped in by the static load sharing.of packets dropped in by the static load sharing.



Minimal Disruption Property Ensures Few Flow RemappingsMinimal Disruption Property Ensures Few Flow Remappings

0 50 100 150 20010

1

102

103

104

105

Flo

ws

Time

Flows appearingFlows persistentFlows remapped

Flows, per iteration: appearing, Flows, per iteration: appearing, persitentpersitent and remapped.and remapped.

Adaptive control loop Adaptive control loop leads on average to:leads on average to:•• less than 0.05% less than 0.05% of the of the appearing flowsappearing flowsremapped per iteration;remapped per iteration;•• less than 0.2% less than 0.2% of the of the persistent flowspersistent flowsremapped per iteration.remapped per iteration.



AHH and ABS on generated trafficAHH and ABS on generated traffic



Adaptive HRW and Adaptive Burst SwitchingAdaptive HRW and Adaptive Burst Switching

Packet drop rates.Packet drop rates. Packet reordering.Packet reordering.

Packet remapping.Packet remapping.



ConclusionConclusion•• ABS better in preventing packet drop ABS better in preventing packet drop •• AHH better in preventing remapping and reordering, AHH better in preventing remapping and reordering, although larger table would improve ABSalthough larger table would improve ABS•• ABS works on much smaller timeABS works on much smaller time--scale scale –– can address can address packet burstspacket bursts•• AHH converges to optimal allocation, but timescale too AHH converges to optimal allocation, but timescale too long for burstslong for bursts•• Best solution probably hybrid Best solution probably hybrid –– under investigation under investigation –– must must be careful to avoid conflicting feedbackbe careful to avoid conflicting feedback--control mechanismscontrol mechanisms



The EndThe End

Thank you!Thank you!

Seminar @ 10:40Seminar @ 10:40



SeminarSeminar



Seminar agendaSeminar agenda

Q & AQ & A

Exercises 1, 2, 3Exercises 1, 2, 3

Internships @ IRCInternships @ IRC

HomeworkHomework

Q & A II.Q & A II.

Cinema?Cinema?



Exercise 1: Mapping DisruptionExercise 1: Mapping DisruptionLet there be a system with M servers of the same Let there be a system with M servers of the same

capacity, and an (M+1)th server be added to the capacity, and an (M+1)th server be added to the system. Compute the amount of mapping system. Compute the amount of mapping disruption with the:disruption with the:

a)a) Contiguous mappingContiguous mapping

b)b) Mod M mappingMod M mapping



Exercise 2: Prove the x2p relationship of HRW mappingExercise 2: Prove the x2p relationship of HRW mapping



Homework: Homework:

1.1. Prove the Prove the mindisruptionmindisruption property of Adaptive HRW property of Adaptive HRW hashinghashing

2.2. Try and design your own adaptive load balancing Try and design your own adaptive load balancing mapping that a) reduces packet reordering within mapping that a) reduces packet reordering within flows, or b) reduces flow remapping. flows, or b) reduces flow remapping.



Q & AQ & A



Internships? Films?Internships? Films?

Thank you!Thank you!

[email protected]@intel.comhttp://www.intelhttp://www.intel--research.net/cambridgeresearch.net/cambridge//

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