configuration optimization tool

DICEHorizon2020Research&InnovationActionGrantAgreementno.644869http://www.dice-h2020.eu

FundedbytheHorizon 2020FrameworkProgrammeoftheEuropeanUnion

ConfigurationOptimizationTool

Pooyan JamshidiImperialCollegeLondon

BigDataTechnologies

Cloud(Priv/Pub)`

DICEFramework

2©DICE

DICEIDE

Profile

Plugins

Sim Ver Opt

DPIM

DTSM

DDSMTOSCAMethodology

Deploy Config Test

Mon

AnomalyTrace

Iter.Enh.

DataIntensiveApplication(DIA)

Cont.Int. FaultInj.

WP4

WP3

WP2

WP5

WP1 WP6- Demonstrators

ConfigurationOptimizationToolThe problem:o BigDatatechnologieshave

100sof tuneableparameterso Aknowledgegapisfacedby

SMEsinconfiguringthesetechnologies

What does the tool do?o Automatically runs

experimentsonDIAso Returnsrecommended

configuration parametersforBigDatatechnologies

3©DICE

Innovation:o AutomateDIAconfiguration

acrossreleasecycleso Priorartfocusesonmanual

configuration

Impact & stakeholders:o Reducecost andtimeof

testing betweenreleaseso SupportoperatorsofDIAs

COToolArchitecture

4©DICE

Configuration Optimisation Tool

performance repository

Monitoring

Deployment Service

Data Preparation

configuration parameters

values


values

Experimental Suite

Testbed

Doc

Data Broker

Tester

experiment timepolling intervalprevious versions

configurationparameters

GP model

Kafka

Vn

V1 V2

System Under Test

historical data

WorkloadGenerator

Technology Interface

Stor

m

Cas

sand

ra

Spar

k

TwoimpementationsofCO:BO4CO,TL4CO

ToolInput(ParametersandOptions)

5©DICE

1- Information about the experiment: budget, config file, duration of each experiment

2- Information about the configuration parameters and their options that testers determine

COToolArchitecture

6©DICE



Monitoring

Deployment Service

Data Preparation


values


values

Experimental Suite

Testbed

Doc

Data Broker

Tester



GP model

Kafka

Vn

V1 V2

System Under Test

historical data

WorkloadGenerator


Stor

m

Cas

sand

ra

Spar

k

OptimizationComponent(Matlab)

7©DICE

- This component select the next configuration to experiment considering the current measurements,

- This continues until optimum configuration located or experimental budget finished. Theoptimizationoverhead

isnegligablecomparingwithmeasurements

This componly relies on rayality free MCRcomponent

COToolArchitecture

8©DICE



Monitoring

Deployment Service

Data Preparation


values


values

Experimental Suite

Testbed

Doc

Data Broker

Tester



GP model

Kafka

Vn

V1 V2

System Under Test

historical data

WorkloadGenerator


Stor

m

Cas

sand

ra

Spar

k

ExperimentalSuite

9©DICE

Thiscomponentrunstheexperimentsandmeasurestheperformanceofthesystemundertest,thedataareflushedtocsvfileandcommunicatedwiththeoptimizationcomponent

COToolArchitecture

10©DICE



Monitoring

Deployment Service

Data Preparation


values


values

Experimental Suite

Testbed

Doc

Data Broker

Tester



GP model

Kafka

Vn

V1 V2

System Under Test

historical data

WorkloadGenerator


Stor

m

Cas

sand

ra

Spar

k

PerformanceRepository

11©DICE

spouts max_spout sorters emit_freq chunk_size message_size throughput latency1 10 1 1 1.00E+05 1000 22657 3.98331 10 1 1 1.00E+05 10000 3596.3 18.4151 10 1 1 1.00E+05 1.00E+05 112.56 217.631 10 1 1 1.00E+06 1000 12273 5.19521 10 1 1 1.00E+06 10000 1174.9 24.2471 10 1 1 1.00E+06 1.00E+05 111.88 205.491 10 1 1 2.00E+06 1000 12024 5.29351 10 1 1 2.00E+06 10000 1151.3 25.0391 10 1 1 2.00E+06 1.00E+05 94.294 220.621 10 1 1 1.00E+07 1000 11552 6.28671 10 1 1 1.00E+07 10000 1228.1 24.9751 10 1 1 1.00E+07 1.00E+05 102.29 236.191 10 1 10 1.00E+05 1000 25978 3.47821 10 1 10 1.00E+05 10000 10112 9.28471 10 1 10 1.00E+05 1.00E+05 1023.8 83.2361 10 1 10 1.00E+06 1000 24147 3.65941 10 1 10 1.00E+06 10000 8400.2 11.8041 10 1 10 1.00E+06 1.00E+05 1197.4 73.7861 10 1 10 2.00E+06 1000 22858 3.71511 10 1 10 2.00E+06 10000 7141.3 10.7551 10 1 10 2.00E+06 1.00E+05 1095.1 78.6241 10 1 10 1.00E+07 1000 22693 4.36371 10 1 10 1.00E+07 10000 6281.5 14.3081 10 1 10 1.00E+07 1.00E+05 951.27 71.4921 10 1 60 1.00E+05 1000 25862 3.85211 10 1 60 1.00E+05 10000 10859 8.64521 10 1 60 1.00E+05 1.00E+05 1128.8 79.8621 10 1 60 1.00E+06 1000 23553 3.90481 10 1 60 1.00E+06 10000 9734.3 9.3451 10 1 60 1.00E+06 1.00E+05 982 66.8521 10 1 60 2.00E+06 1000 25408 3.57381 10 1 60 2.00E+06 10000 7993.9 9.2784

ConfigurationMetrics Measured

The performance repository mediates between the optimization component and experimental suite

TechnologySupport

13©DICE



Monitoring

Deployment Service

Data Preparation


values


values

Experimental Suite

Testbed

Doc

Data Broker

Tester



GP model

Kafka

Vn

V1 V2

System Under Test

historical data

WorkloadGenerator


Stor

m

Cas

sand

ra

Spar

k

Technologies:Storm,Spark,Cassandra