The Art of CPU-Pinning: Evaluating and Improving the

The Art of CPU-Pinning:Evaluating and Improving the

Performance of Virtualization and Containerization PlatformsDavoodGhatrehSamani,Chavit Denninart

JosephBacik†MohsenAmini Salehi‡

HighPerformanceCloudComputingLab(HPCC)SchoolofComputingandInformaticsUniversityofLouisianaLafayette

1

Introduction

• Executionplatforms1. Bare-Metal(BM)2. HardwareVirtualization(VM)3. OSVirtualization(containers,CN)

• Choosingaproperexecutionplatform,basedontheimposedoverhead▪ ContainerontopofVM(VMCN)isnotstudiedandcomparedtootherplatformsindepth

2

Introduction

• Overheadbehaviorandtrend▪ Differentexecutionplatforms(BM,VM,CN,VMCN)

▪ Differentworkloadpatterns(CPUintensive,IOintensive,etc.)

▪ Increasingcomputeresources▪ Computetuningapplied(CPUpinning)

• Cloudsolutionarchitectchallenge:▪ WhichExecutionplatformsuitswhatkindofworkload

3

Hardware Virtualization (VM)

• Operatesbasedonahypervisor• VM:aprocessrunninginthehypervisor• HypervisorhasnovisibilitytoVM’sprocesses

• KVM:apopularopen-sourcehypervisorAWSNitroC5VMtype

4

OS Virtualization (Container)

• LightweightOSlayervirtualization• Noresourceabstraction(CPU,Memory,etc.)

• HostOShascompletevisibilitytothecontainerprocesses

• Container=namespace+cgroups• Docker:Themostwidelyadoptedcontainertechnology

5

VM vs Container

6

CPU Provisioning in Virtualized Platform

• Default:Timesharing▪ Linux:CompletelyFairScheduler(CFS)▪ AllCPUcoresareutilizedevenifthere

isonlyoneVMinthehostortheworkloadisnotheavy

▪ EachCPUquantumdifferentsetofCPUcores

▪ CalledVanillamodeinthisstudy• Pinned:FixedsetofCPUcoresforall

quantum▪ Overridedefaulthost/hypervisorOS

scheduler▪ Processisdistributedonlyamong

thosedesignatedCPUcores

7

Execution Platforms

8

Application types and measurements

• Measuredperformancemetric▪ TotalExecutionTime

• Overheadratio▪ !"#$%&##(#)*+,-.+,/#-00#$#123+4#56%+0-$/

!"#$%&##(#)*+,-.+,/#-02%$#7/#+%6

• Performancemonitoringandprofilingtools▪ BCC(BPFCompilerCollection:cpudist,offcputime),iostat,perf,

htop,top

9

Configuration of instance types

• Hostserver:DELLPowerEdgeR830▪ 4×IntelXeonE5-4628Lv4

o Eachprocessoris1.80,35MBcacheand14processingcores(28threads)

o 112homogeneouscores▪ 384GBmemory

o 24×16GBDDR4DRAM▪ RAID1(2×900GBHDD10k)storage.

10

Motivation

• IndepthstudyofcontainerontopofVM(VMCN)

• Comparingdifferentexecutionplatforms(BM,VM,CN,VMCN)alltogather

• Reallifeapplicationswithdifferentworkloadpatterns

• Findinganoverheadtrendbyincreasingresourceconfigurations

• InvolvingCPUpinningintheevaluation

11

Contribution to this work

• Unveiling▪ PSO(PlatformSizeOverhead)▪ CHR(ContainertoHostcoreRatio)

• LeveragePSOandCHRtodefineoverheadbehaviorpatternfor▪ Differentresourceconfigurations▪ Differentworkloadtypes

• Asetofbestpracticesforcloudsolutionarchitects▪ Whichexecutionplatformsuitswhatkindof

workload

Experiment and analysis: Video Processing Workload Using FFmpeg

• FFmpeg:Widelyusedvideotranscoder▪ Veryhighprocessingdemand▪ Multithreaded(upto16cores)▪ Smallmemoryfootprint▪ RepresentativeofaCPU-intensiveworkload

• Workload:▪ Function:codecchangefromAVC(H.264)to

HEVC(H.265)▪ Sourcevideofile:30MBHDvideo▪ Meanandconfidenceintervalacross20timesof

executionforeachplatformcollected

Experiment and analysis: Video Processing Workload Using FFmpeg

Experiment and analysis: Parallel Processing Workload Using MPI

• MPI:Widely-usedHPCplatform▪ Multi-threaded▪ ResourceusagefootprinthighlydependsontheMPIprogram

• Workload▪ Applications:MPI_Search,Prime_MPI▪ Computeintensive,however,communicationbetweenCPUcoresdominatesthecomputation

▪ Meanandconfidenceintervalacross20timesofexecutionforeachplatformcollected

Experiment and analysis: Parallel Processing Workload Using MPI

Experiment and analysis: Web-based Workload Using WordPress

• WordPress▪ PHP-basedCMS:ApacheWebServer+MySQL▪ IOintensive(networkanddiskinterrupts)

• Workload▪ AsimplewebsiteissetuponWordPress▪ Browsingbehaviorofawebuserisrecorded▪ 1,000simultaneouswebusersaresimulated

o ApacheJmeter▪ Eachexperimentisperformed6times▪ Meanexecutiontime(responsetime)oftheseweb

processesisrecorded

Experiment and analysis: Web-based Workload Using WordPress

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Experiment and analysis: Web-based Workload Using WordPress

NoSQL Workload using Apache Cassandra

• ApacheCassandra:▪ DistributedNoSQL,BigDataplatform▪ Demandscompute,memory,anddiskIO.

• Workload▪ 1,000operationswithinonesecond

o Cassandra-stresso 25%Write,85%Reado 100threads,eachonesimulatingoneuser

▪ Eachexperimentisrepeated20times▪ Averageexecutiontime(responsetime)ofallthe

synthesizedoperations.

NoSQL Workload using Apache Cassandra

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NoSQL Workload using Apache Cassandra

22

Cross-Application Overhead Analysis

• Platform-TypeOverhead(PTO)• Resourceabstraction(VM)• Constanttrend• Pinningisnohelpful

• Platform-SizeOverhead(PSO)▪ DiminishedbyincreasingthenumberofCPUcores▪ Specifictocontainers▪ JustreportedbyIBMforDocker(Websphere tuning)• Pinningishelpfulalot

Parameters affecting PSO

1. ContainerResourceUsageTracking• cgroups

2. Container-to-HostCoreRatio(CHR)

• CHR=AssignedcorestothecontainerTotalnumberofhostcores

3. IOOperations4. Multitasking

Impact of CHR on PSO

• Lower value of CHR imposes a larger overhead (PSO)

• Application characteristics define the value of CHR

• CPU intensive• 0.14 < 𝐶𝐻𝑅 < 0.28

• IO intensive: higher• 0.28 < 𝐶𝐻𝑅 < 0.57

Container Resource Usage Tracking

• OSschedulerallocatesallavailableCPUcorestotheCNprocess

• cgroupscollectsusagescumulatively• EachschedulingeventhasdifferentCPUallocationforthatCN

• cgroupsisanatomic(kernelspace)process• Containerhastobesuspendedwhileaggregatingresource

• OSschedulingenforcesprocessmigration,cgroupsenforceresourceusagetracking-- Synergistic

The Impact of Multitasking on PSO

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The Impact of IO operations on PSO

Experimental Setup: MPI task

• CPU pinning can mitigate this kind of overhead

Summary

Experimental Setup: MPI task

1. Applicationcharacteristicisdecisiveontheimposedoverhead

2. CPUpinningreducetheoverheadforIO-boundapplicationsrunningoncontainers.

3. CHRplaysasignificantroleontheoverheadofcontainers

4. ContainersmayinducehigheroverheadincomparingtoVMs

5. ContainersontopofVMs(calledVMCN)imposealoweroverheadforIOintensiveapplications

Best Practices

Experimental Setup: MPI task

1. Avoidsmallvanillacontainers2. UsepinningforCPU-boundcontainers3. NotworthwhiletousepinningforCPU-boundVMs4. UsepinningforIOintensiveworkloads5. CPUintensiveapplications:0.07<𝐶𝐻𝑅 <0.146. IOintensiveapplications:0.14<𝐶𝐻𝑅 <0.287. UltraIOintensiveapplications:0.28<𝐶𝐻𝑅 <0.57