con10851 raghavan-oow cloud analytics kss18

8/17/2019 CON10851 Raghavan-OOW Cloud Analytics KSS18

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%egal "isclaimer

Intel technologies& features and !ene'ts depend on system con'guration and may re(uire esoftware or serice actiation) Performance aries depending on system con'guration) *o cocan !e a!solutely secure) Chec+ with your system manufacturer or retailer or learn more at i

Software and wor+loads used in performance tests may hae !een optimized for performancmicroprocessor Performance tests, such as SSmar+ and -o!ile-ar+, are measured using spsystems, components, software, operations and functions) Any change to any of those factorresults to ary) ou should consult other information and performance tests to assist you in fucontemplated purchases, including the performance of that product when com!ined with othmore complete information a!out performance and !enchmar+ results, isit www)intel)com/!

0ests document performance of components on a particular test, in speci'c systems) "i1eresoftware, or con'guration will a1ect actual performance) Consult other sources of informatio

performance as you consider your purchase)

.or more complete information a!out performance and !enchmar+ results, isit http2//www)i!enchmar+s

Intel, the Intel logo, Intel Inside, 3eon are trademar+s of Intel Corporation in the 4)S) and/or o5Other names and !rands may !e claimed as the property of others)

6 $789 Intel Corporation)

http://www.intel.com/benchmarkshttp://www.intel.com/benchmarkshttp://www.intel.com/benchmarkshttp://www.intel.com/benchmarkshttp://www.intel.com/benchmarkshttp://www.intel.com/benchmarks


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Classroom 0raining

%earning

Su!scription

%ie


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Session Sureys

=elp us help you>>?e inite you to ta+e a moment to gie us your session feedfeed!ac+ will help us to improe your conference)

Please !e sure to add your feed!ac+ for your attended sessusing the -o!ile Surey or in Schedule uilder)


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Agenda

Oracle Pu!lic Cloud

• Oeriew and Architecture

• IaaS, PaaS, Storage *etwor+ing

Optimizing the Cloud

• ProDect Apollo

•

Intel 3eon Processor E9F$B77 : Product .amily• Early Results

A spoonful of analytics

• Analytics for Cloud performance optimization


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Oracle Compute Cloud Serice "eliOeriew and Architecture

Core OCCSOCCS is Foundation for NOracle PaaSSaaS Servic


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Secure. !elia"le. Low Cost.

StorageElastic Storage

Compute"edicated Compute

NetworSoftwareFde'ned

IaaS2 eneral Purpose, Engineered Systems

Oracle InfrastructureFasFaFSer


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$ardware %solation%aunch "edicatedInstances on singleFtenanthardware with networ+isolation

&ctive '( !ecoveryCon'gure =A Policies toautomatically recoerfailed


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Oracle PlatformFasFaFSerice

IaaS API

PaaS Serice -anager

loc+ Storage O!Dect Storage Compute

"ata!ase Laa "eeloper -o!ile "ocumentsSocial

*etwor+ig "ata I P-essaging


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PaaS Serices Options

Customer managed serices

Customer has


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Oracle Storage Cloud Serices Accessi!le Secure 2 EnterpriseFgr

protection and priacy

Scala!le 2 O*F"emand Relia!le 2 Redundancy

"ata =A StandardsFased 2 Op

compati!le RES0 API data management

=y!rid storage tiers

• ,ac#up

4ser !ac+up, PaaStarget

• &rchive

Archie for longFte

compliance needs

OpenStac+ S?I.0 API

lo!al *amespaceArchie lacial MOS

Eentual Consistency

R-A*Storageatewa

y

Any *AS

or SA*


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Optimizing the cloudProDect Apollo


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ProDect Apollo

"elier predicta!le high

performance for applicationsrunning on Oracle Cloud

Characterize the cloud usingOracle Cloud wor+loads

Optimize the cloud to delierma#imum performance for thewor+load

Innoate, deelop newtechnologies

enerate !lueprint of anoptimized data center

Oracle Cloud S 0uning

Intel Cloud 0echn


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Components in Cloud

Rac+sSerer

s

Powe 0heStorage Switches


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?hat does Cloud performance meanT

Application performance UCompute, Storage and *etwor+,

Scala!ilityV

-ultiFtenancy, Predicta!leperformance,

Security, Elasticity,Composa!ility, =igh Aaila!ility V

Optimal resource usage,Power, Space, Cooling V

Application"eeloper

Administrator

Serice Proider

Cl d t t I t l


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Laa as a Serice

Intel 3eon E9 U $BJJ:

OraSto

"ata!ase as aSerice

Infrastructure, Power andCooling

Cloud setup at Intel

$777 compute cores$G 0 of RA-

877 0 of Storage

Cloud Incu!ator atIntel


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E#tend to other Intel Cloud 0echnologie

Reference82 http2//newsroom)intel)com/community/intelXnewsroom/!log/$789/7G/$H/intelFandFmicronFproduceF!rea+throughFmemoryReference$2 http2//www)intel)com/content/www/us/en/architectureFandFtechnology/intelFrac+FscaleFarchitecture)html

.uture plans to ealuate Intel Cloud technologies in -emo*etwor+, Security, PowerV)


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Optimizing the cloudIntel 3eon Processor E9F$B77 : Product

.amily


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.eature 3eon processor E9F$B77 $ product

family3eon processor E

fa

Cores/0hreads per soc+et 4p to 8$ Cores / $; 0hreads 4p to 01 Core

%astFleel Cache Y%%CZ 4p to :7 - 4p to

-a# -emory SpeedY-0/sZ 4p to 8HBB 4p to

MPI Speed Y0/sZ $# MPI 8)8 channels B);, G)$, H)7 $# MPI 8)8 chan

-a# "I-- Capacity 4p to 8$ Slots/Processor

PCIe5 %anes /Controllers/Speed

4p to ;7 / 87 / PCIe5 :)7 Y$)9, 9, H 0/sZ

0"P Y?Z 8:7, 889, J9? 0258 06

&'; A


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97[ -ORE%astFleelcache

Cores 0hreads

I-PRO


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Optimizing the cloudEarly Results

Ch i i h Cl d


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Characterizing the Cloud• -odel Oracle Cloud wor+loads with multiple simultaneous applic

• Composed of2

• LaaS "aaS application wor+load

• CP4 , IO *etwor+ stress

• RealFtime data gathered from across the stac+

• Application performance

• Software logs U Laa, Application Serer, "ata!ase

• Cloud Platform / OS

•

Statistics from


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Early optimization results

?e can achiee signi'cantperformance gains from our early

optimization e1orts of OracleCloud for Intel 3eon $BJJ :)

4p to 0.5< for response timesensitie apps5

4p to 0.6


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etter predicta!ility and scaling

y enhancing resource

allocation mechanism we canachiee2

-ore predicta!leperformance5

%inear scaling5 F %inearincrease in performance withincrease in OCP4s

7 $ ;

7

877

$77

:77

;77

977

B77

G77

Performance

5 As measured !y serer side Laa wor+load in Intel la!oratory for prede'ned Cloud wor+load con'guration5 Software and wor+loads used in performance tests may hae !een optimized for performance only on Intel microprocessors) PerformSSmar+ and -o!ile-ar+, are measured using speci'c computer systems, components, software, operations and functions) Any changmay cause the results to ary) ou should consult other information and performance tests to assist you in fully ealuating your contemincluding the performance of that product when com!ined with other products)5 .or more information go to http2//www)intel)com/performance/datacenter)

http://www.intel.com/performance/datacenterhttp://www.intel.com/performance/datacenterhttp://www.intel.com/performance/datacenter


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A spoonful of analyt4se of analytics for cloud performance optimization


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A Scenario for Analytics

?%S Application Serers in the Cloud

Simulated 4sers

"ata collected from Simulated 4sers and Serers

&pps

&pp 0

&pp 6

=

Common

Common 0

=

(id)>ier


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Approach

(ultiple Platforms for Processing

!aw data

Chec# Processing

uality

Compute

Statistics

Posterior

&nalysis

Python !

>idy /ata

Format

(erge /ata sets over

>ime

!aw data!aw data

>idy /ata

Format>idy /ata

Format


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System -onitoring

• e)g) Laa logsApplication

• system actiity report YsarZSystem/OS/


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ar!age CollectionYCZ "ata

+very aphas its o(any Al

2015-07-24T13:53:13.141-0700: 75.604: [GC [PSYoungGen:

1133359K->165347K(1223680K)] 1133447K->165470K(4020224K)

0.1085510 !e"!] [T#$e!: u!e%&0.59 !'!&0.08 %e&0.11 !e"!]

2015-07-24T13:53:22.445-0700: 84.909: [GC [PSYoungGen:

1214435K->168469K(1223680K)] 1214558K->168672K(4020224K)

0.1442510 !e"!] [T#$e!: u!e%&0.97 !'!&0.14 %e&0.14 !e"!]

2015-07-24T13:53:31.495-0700: 93.959: [GC [PSYoungGen:

1217557K->149712K(1199104K)] 1217760K->149923K(3995648K)

0.1272560 !e"!] [T#$e!: u!e%&0.75 !'!&0.01 %e&0.13 !e"!]

2015-07-24T13:53:35.700-0700: 98.163: [GC [PSYoungGen:

1198800K->145280K(1185792K)] 1199011K->145499K(3982336K)

0.0946850 !e"!] [T#$e!: u!e%&0.78 !'!&0.02 %e&0.10 !e"!]

2015-07-24T13:53:41.997-0700: 104.460: [GC [PSYoungGen:

1131904K->88361K(1192448K)] 1132123K->146072K(3988992K)

0.1296750 !e"!] [T#$e!: u!e%&1.03 !'!&0.14 %e&0.13 !e"!]

2015-07-24T13:53:51.739-0700: 114.203: [GC [PSYoungGen:

1074985K->118373K(1202176K)] 1132696K->228993K(3998720K)0.2367950 !e"!] [T#$e!: u!e%&1.00 !'!&0.09 %e&0.24 !e"!]

2015-07-24T13:53:59.035-0700: 121.498: [GC [PSYoungGen:

1116261K->145330K(1193984K)] 1226881K->266899K(3990528K)

0.2270100 !e"!] [T#$e!: u!e%&0.59 !'!&0.02 %e&0.23 !e"!]

2015-07-24T13:54:03.826-0700: 126.289: [GC [PSYoungGen:

1143218K->53006K(1190912K)] 1264787K->233618K(3987456K)

0.0936990 !e"!] [T#$e!: u!e%&0.56 !'!&0.09 %e&0.10 !e"!]


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-essy, semiFstructured data T

\ Column headers are alues, not aria!le names)

\ -ultiple aria!les are stored in one column)\


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"ata Processing, !y ]=adley ?ic+hamChief "ata Scientist at RStudio

"ata Processing is the most essential part of data analysis)encompasses actiities li+e outlier detection, data parsing,alue imputation, etc)

=adley&s contri!ution to the "ata Analytics society2

Proposed a guideline for processed dataF_ ]tidy data for

"eeloped pac+ages in R, that would ma+e data process

dplyr8 tidyrggplot


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C %ogs Q SAR Q E-O* Q V

0idy "ata .ormat >>>

d f i id


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Adantages of Aggregating 0idy "ata

Analysis made possi!le)

"ata isualization !ecomes handy)Easy correlation among the arious metrics on di1erent sys

Comparison of trends of metrics across systems)

S


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Summary

Oracle Cloud

ProDect Apollo2 Loint IntelFOracle Colla!oration

Cloud Performance Analysis for Your Applications

• "ata Collection

• "ata Cleansing

• Analytics

"emo2 I*0ERAC0I


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"emo2 I*0ERAC0I


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con10851 raghavan-oow cloud analytics kss18

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