information-agnostic flow scheduling for commodity...

KaiChenSINGGroup,CSEDepartment,HKUSTMay16,2016@StanfordUniversity

Information-AgnosticFlowSchedulingforCommodityDataCenters

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SINGTestbed Cluster

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OpticalCircuitSwitch(x5)

ElectricalPacketSwitch,10G(x3)

ElectricalPacketSwitch,1G(x10)

Commodity rackservers,DellPowerEdgeR320(x100)

• NetworkStructures– OSA,thefirst-everallopticaldatacenterarchitecture(NSDI’12,TON’14)– MegaSwitch,non-blockingopticaldatacenterfabric

• Protocols/Algorithms– XPath,intra-DCroutingcontrol(NSDI’15,TON’15)– Amoeba,inter-DCdatatransfers(EuroSys’15)– PIAS,information-agnosticflowscheduling(HotNets’14,NSDI’15)– Karuna,mix-flowscheduling(SIGCOMM’16)– CODA,coflow schedulinginthedark(SIGCOMM’16)– MQ-ECN,congestioncontrolformulti-queueDC(NSDI’16)– CuttingTailLatency– Shallow-bufferManagement– ......

• Platform:DistributedComputingEngineforBigDataApps– network-enabledlargescaledistributed,parallelcomputingplatformsfor

bigdataanalyticsandmachinelearning

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SING’sResearch:ABottom-upApproach

Goal:simple,effective,implementablenetworkedsystems!

PIAS:PracticalInformation-AgnosticFlowScheduling[USENIXNSDI’15]

• Cloudapplications– Desirelowlatencyforshortmessages

• Goal:Minimizeflowcompletiontime(FCT)– Manyflowschedulingproposals…

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TheState-of-the-artSolutions

• PDQ[SIGCOMM’12]

• pFabric [SIGCOMM’13]

• PASE[SIGCOMM’14]• …

All assume prior knowledge of flow size information toapproximate ideal preemptive Shortest Job First (SJF)with customized network elements

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Notfeasibleforsomeapplications6






Hardtodeployinpractice7

Question

Without prior knowledge of flow size information, howto minimize FCT in commodity data centers?

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DesignGoal1


Information-agnostic: not assume a priori knowledge offlow size information available from the applications

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DesignGoal2


FCT minimization: minimize average/tail FCTs of shortflows & not adversely affect FCTs of large flows

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DesignGoal3


Readily-deployable: work with existing commodityswitches & be compatible with legacy network stacks

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Question


Ouranswer:PIAS

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PIAS’SDESIGN

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DesignRationale

• PIASperformsMulti-LevelFeedbackQueue(MLFQ)toemulateShortestJobFirst

Priority1

Priority2

PriorityK

……

High

Low14

DesignRationale

• PIASperformsMulti-LevelFeedbackQueue(MLFQ)toemulateShortestJobFirst

Priority1

Priority2

PriorityK

……

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SimpleExampleIllustratingPIAS

Congestion

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Priority1

Priority2

Priority4

Flow1with10packetsandflow2with2packetsarrive

Priority3

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Priority1

Priority2

Priority4

Flow1and2transmitsimultaneously

Priority3

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Priority1

Priority2

Priority4

Flow2finisheswhileflow1isdemotedtopriority2

Priority3

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Priority1

Priority2

Priority4

Flow3with2packetsarrives

Priority3

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Priority1

Priority2

Priority4

Flow3and1transmitsimultaneously

Priority3

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Priority1

Priority2

Priority4

Flow3finisheswhileflow1isdemotedtopriority3

Priority3

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Priority1

Priority2

Priority4

Flow4 with2packetsarrives

Priority3

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Priority1

Priority2

Priority4

Flow4 and1transmitsimultaneously

Priority3

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Priority1

Priority2

Priority4

Priority3

Flow4 finisheswhileflow1isdemotedtopriority4

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Priority1

Priority2

Priority4

Priority3

Eventually,flow1finishesinpriority4

With MLFQ, PIAS can emulate Shortest Job First withoutprior knowledge of flow size information

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Howtoimplement?

Priority1

Priority2

PriorityK

……

• Strictpriorityqueueingonswitches• Packettaggingasashimlayeratendhosts

• 𝐾priorities:𝑃$ 1 ≤ 𝑖 ≤ 𝐾

• 𝐾 − 1 demotionthresholds:𝛼*(1 ≤ 𝑗 ≤ 𝐾 − 1)

• Thethresholdtodemotepriorityfrom𝑃*./ to𝑃*is𝛼*./

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Howtoimplement?

Priority1

Priority2

PriorityK

……


1

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Howtoimplement?

Priority1

Priority2

PriorityK

……


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Howtoimplement?

Priority1

Priority2

PriorityK

……


2

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• Thresholdsdependon:– Flowsizedistribution– Trafficload

• Solution:– SolveaFCTminimizationproblemtocalculatedemotionthresholds

• Problem:– Trafficishighlydynamic

DetermineThresholds

Trafficvariations->Mismatchedthresholds

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ImpactofMismatches

HighLow

10MB

10MB

20KB

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• Whenthethresholdisperfect(20KB)

ImpactofMismatches

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• Whenthethresholdistoosmall(10KB)

ImpactofMismatches

Increasedlatencyforshortflows

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• Whenthethresholdistoolarge(1MB)

ImpactofMismatches

Increasedlatencyforshortflows

LeverageECNtokeeplowbufferoccupancy

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• Whenthethresholdistoosmall(10KB)

HandleMismatches

ECNcankeeplowlatencyIfweenableECN

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• Whenthethresholdistoolarge(1MB)

HandleMismatches

ECNcankeeplowlatencyIfweenableECN

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PIASin1Slide

• PIASpackettagging– Endhostsmaintainflowstatesandmarkpacketswithpriorities

• PIASprioritization– Switchesenforcestrictpriorityqueueing

• PIASratecontrol– EmployECN-basedtransport(e.g.,DCTCP)tohandlemismatch

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TestbedExperiments

• PIASprototype– http://sing.cse.ust.hk/projects/PIAS

• TestbedSetup– AGigabitPronto-3295switch– 16Dellservers

• Benchmarks– Websearch(DCTCPpaper)– Datamining(VL2paper)– Memcached

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SmallFlows(<100KB)

WebSearch DataMining

Compared to DCTCP, PIAS reduces averageFCT of small flows by up to 47% and 45%

47%45%

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NS2SimulationSetup

• Topology– 144-hostleaf-spinefabricwith10G/40Glinks

• Workloads– Websearch(DCTCPpaper)– Datamining(VL2paper)

• Schemes– Information-agnostic:PIAS,DCTCPandL2DCT– Information-aware:pFabric

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OverallPerformance


PIAS has an obvious advantage over DCTCP andL2DCT in both workloads.

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SmallFlows(<100KB)

Simulations confirm testbed experiment results


40%- 50%improvement

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ComparisonwithpFabric

PIAS only has 4.9% performance gap to pFabricfor small flows in data mining workload


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Conclusion

• PIAS:simple,practicalandeffective– Notassumeflowinformationfromapplications

– EnforceMulti-LevelFeedbackQueuescheduling

– Usecommodityswitches&legacynetworkstacks

Information-agnostic

FCTminimization

Readilydeployable

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Thanks!

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information-agnostic flow scheduling for commodity...

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