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PerfCenter and AutoPerf: Tools and Techniques for Modeling and Measurement of the Performance of Distributed Applications

Varsha Apte

Faculty Member, IIT Bombay

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Example: WebMail Application (ready to be deployed)

IMAP server

Ad Server

Authentication Server,

SMTP Server

Web Server

WAN

User request

Several interacting components

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Several Usage ScenariosExample: Login

Browser Web Authentication IMAP SMTPUser/Password

Verify_passwd

Send_to_auth

GenerateHtml

list_messageCall list_message of IMAP server

GenerateHtml

0.2

0.8

Res

pons

e T

ime

Performance Goals during deployment:•User perceived measures: response time, request drops (minimize)•System measures: throughput, resource utilizations (maximize)

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Deploying the application in a Data Center

What should be the configuration of the Web server? (Number of threads, buffer size,…)

On which machines should IMAP server be deployed? The Web Server?

How will the network affect the performance? (LAN vs WAN)

How many machines? Machine configuration? (how many CPUs, what speed, how many disks?)

Determining host and network architecture

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Input specifications

Machines and Devices

Software Components

Network Params

Deployments

Scenarios

Parser

Queuing Model

Simulation Tool Analytical Tool

Ref MASCOTS 07

Output Analysis

PerfCenter: Modeling Tool

Inbuilt functions and constructs aid datacenter architect to analyze and modify the model

PerfCenter generates underlying queuing network modelPerfCenter solves

the model

Architect specifies the model

PerfCenter code can be downloaded from http://www.cse.iitb.ac.in/perfnet/perfcenter

*

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Capacity analysis for WebMail

Maximum throughput achieved is 30 requests/sec

Graph for Response time performance with increase in number of users

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Autoperf: a capacity measurement and profiling tool

Focusing on needs of a performance modeling tool

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Input requirement for modeling tools Usage Scenarios Deployment details Resource Consumption Details – e.g.

“login transaction takes 20 ms CPU on Web server”Usually requires measured data

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Performance measurement of multi-tier systems

Two goals: Capacity Analysis:

Maximum number of users supported, transaction rate supported, etc

Fine grained profiling for use in performance models

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Measurement for Capacity analysisMeasurement for Capacity analysis

Clients running load generators

System Test EnvironmentSystem Test Environment

Servers running system under test

GenerateRequests

E.g.:HttperfFloodSilk PerformerLoadRunner

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…….Measurement for capacity .Measurement for capacity analysis – Answers providedanalysis – Answers provided

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Given such data, models can “extrapolate” and predict performance at volume usage (e.g. PerfCenter).

Measurement for modelingMeasurement for modeling

Web ServerApp Server 1

App Server 2

ResourceConsumption profile

10ms

20ms

40ms

45ms

LAN

Clients running load generators

GenerateRequests

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Generate Load

ProfileServers

1 2

3Collect clientstatistics Collect server

statistics

Client

Correlate & display

Introducing: Introducing: AutoPerfAutoPerf Servers

AutoPerfAutoPerf

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AutoPerfAutoPerfDeployment Information of servers

Web transaction workload description

Fine grained server sideResource profiles

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Future enhancements PerfCenter/AutoPerf:

Various features which make the tools more user-friendly

Capability to model/measure performance of virtualized data centers

Many other minor features Skills that need to be learned/liked:

Java programming (both tools are in Java) Discipline required to maintain/improve large software

Working with quantitative data

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What is fun about this project?

Working on something that will (should) get used.

New focus on energy and virtualization –both exciting fields

Many, many, algorithmic challenges Running simulation/measurement in efficient

ways

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Work to be done by RA

Code maintenance Feature enhancement Write paper(s) for publication, go to conferences,

present them Creating web-pages and user groups, answering

questions Help in popularizing the tool, demos, etc Pick a challenging problem within this domain as

M.Tech. project, write paper (s), go to conferences!

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Thank you/Questions

This research was sponsored by MHRD, Intel Corp, Tata Consultancy Services and IBM faculty award 2007-2009

PerfCenter code can be downloaded from http://www.cse.iitb.ac.in/perfnet/perfcenter

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Request Arrival

Get SoftServer Get DeviceGet Network

Link

InstanceFree

BufferFull

Drop

ServiceRequest

EnQueue

Y

YN

N

SoftServerDevice

Service ReqNetworkLinkService Req

Queue Class

Simulator: Queue ClassAll resources like devices, soft servers and network link are abstracted queues

Discrete event simulator implemented in Java

Supports both open and

closed arrivals

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Simulator: Synchronous callsServer1 Server2 Server3 Server4

Thread Busy

Thread waiting

Server1-t

Server2-t

Server3-t

PerfCenter Stack

User

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Simulator Parameters

PerfCenter Simulates both open and closed systems

loadparmsarate 10end

loadparms noofusers 10 thinktime exp(3)end

Model parameters

modelparmsmethod simulationtype closednoofrequest 10000confint falsereplicationno 1end

Independent replication method for output analysis

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Deployments dply3 and dply4

H1

Cpu%

H2

Cpu%

H3

Cpu%

H4

CPU%

IMAP

Host Disk%

H2

Disk%

dply3 77.6 17.7 23.9 NA 44.2 18.7

dply4 53.8 18.4 27.7 NA 47.1 19.5

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Deployment summary

H1

Cpu

%

H2

Cpu

%

H3

Cpu

%

H4

CPU

%

IMAP

Host Disk

%

H2

Disk

%

dply1 98.1 8.2 NA NA 41.2 8.8

dply2 67.5 15.9 48.6 75.0 80.6 17.0

dply3 77.6 17.7 23.9 NA 44.2 18.7

dply4 53.8 18.4 27.7 NA 47.1 19.5

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Simulator: Dynamic loading of Scheduling policy

/Queue/SchedulingStartergy/

/FCFS.class

/LCFS.class

/RR.class

host host[2]cpu count 1cpu schedp fcfscpu buffer 9999end

host host[2]cpu count 1cpu schedp rrcpu buffer 9999end

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Using PerfCenter for “what-if” analysis

Scaling up Email Application To support requests arriving at rate

2000 req/sec

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Step 1 Step 2

diskspeedupfactor2 =20diskspeedupfactor3=80deploy web H4set H1:cpu:count 32set H4:cpu:count 32set H3:cpu:count 12set H2:cpu:count 12cpuspeedupfactor1=2cpuspeedupfactor3=4cpuspeedupfactor4=2

host H5

cpu count 32

cpu buffer 99999

cpu schedP fcfs

cpu speedup 2

end

deploy web H5

set H2:cpu:count 32

set H3:cpu:count 18

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Summary

H1 H2 H3 H4 H5

Step1 CPU Count 32 12 12 32

Cpu Utilization 88 100 75.8 87.7

CPUSpeedUp 2 1 4 3

Disk Speedup 20 80

Disk utilization 51.6 46.5

Step2 CPU Count 32 32 18 32 32

Cpu Utilization 64.5 55.9 57.0 63.1 58.7

CPUSpeedUp 2 1 4 2 2

Disk Speedup 20 80

Disk utilization 58 52.6

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Identifying Network Link Capacity

256Kbps 1Mbps

Lan1 to lan2 20.1% 5.1%

Lan2 to lan1 18.7% 4.8%

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Limitations of standard tools Do not perform automated capacity analysis

Need range of load levels to be specified Need duration of load generation to be specified Need the steps in which to vary load to be specified Report only the throughput at a given load level, but not

the maximum achievable throughput and saturation load level.

Tools should take as input a better workload description (CBMG) rather than just the percentage of virtual users requesting each type of transaction.

Do not perform automated fine grained server side resource profiling.