Optimizing Web20 App Performance

8/14/2019 Optimizing Web20 App Performance

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Slow Cool, Aint Cool

Hon Wong, CEO, Symphoniq Corporation

Optimizing 'Web 2.0' Application Performance


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Ajax Web 2.0 apps

newopportunities

newperforma nce ma nag ement c hallenges

Rea l userapproac h needed

New approa c h in ac tion

Q&A

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Agenda


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The Evolving Web

3

Web 2.0Web as c omputing

platform

Users add va lue


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All In The Name of the End User

4Web 1.0 Web 2.0 And beyond

BrittleArchitecturesRigidTaxonomiesWebasInformation

Source

RichInternetApplications(RIA)LooselyCoupledArchitectures

LooseFolksonomiesWebasInformation

Collaboration

TheNextBigKillerApp/FeatureUbiquitousComputing

andAccessDynamicArchitectures

SemanticWebWebasInformation

Synthesis


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Cha llenges of the Ric h User

Experience

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NewUse Cases

Distributed , high volumepub lish/ subsc ribe mod el

Orga nic , user d riven a pp lic a tionevo lution with no QA p rocess

Reuse of existing interna l or 3rd partyservices or c ontent

Loose folksonomies rep lace rig id taxonomies in d a ta struc tures

NewTec hnologies

Comp lica ted Arc hitec tures:

SOA, Web Services, REST, XML

More c ha tty p rotocols / higherfreq uency of connec tions

More log ic on the c lient, c rea tinganother potentia l bottlenec k

RIA c onta iners reduc e visib ility intoclient experience


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Dea ling with the Cha llenge

Web App DB

Externa l App

Log file analysis

HTTP watch

Network sniffers

Load testers

Server monitors

Monitoring services

Many more

Traditional tools & techniques generateoceans of data no solutions


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And Even if A Prob lem is

Discovered

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

5 days

While most problems get solved in less than a day,30% of problems take more than a day to solve.

Forrester Research


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Performanc e Management:

Prac tic a l Question # 1

When user satisfac tion has direc t business impac t, do you havethe luxury of blind ly assuming users are satisfied with

application performance?

Living is a form of not being sure, notknowing what next or howWe guess. We

may be wrong, but we take leap after leap in

the dark.

Agnes de Mille


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"I'm an ocean, because I'm really deep.If you search deep enough you can find

rare exotic treasures.

Christina Aguilera

Performance Management:


When business happens in Web time, do you have time to

search oc eans of performance data to p in-point the c ause ofslowdowns?

10


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Strive for continuous

improvement, instead ofperfection.

Kim Collins

When complexity and high-speed c hangemake perfec tion unatta inab le, do you have the ac tionab leinformation required to drive performance improvements?

Performance Management:

http://www.discoverstkitts.com/kim_gallery.htm


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Holistic Approac h to

Performance Management

Real User Monitoring

How can I avoid being

blind-sided by

performance issues?

Which users are being

affected?

How can I troubleshoot

specific user issues?

Web App Performance Service Level Assurance

Why is my application

slow?

Which tier is causing the

slowdown?

Is it inside or outside the

data center?

How can I recreate orvalidate problems?

What is the impact of

performance problems

on the business?

How do I link

performance criteria to

specific business units?

Web App DB


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Bottom Line Impac t o f

Do Nothing

Wasted ResourcesCosts 10x to fix the

problem in production

Slow = Off

Wasted IT

budget

Inadequate tools

to detect and

diagnose

problems

Blame gametriage

Distraction from core

business

Compromisestrategic

initiatives

Blindsided by

performanceissues

Brand damage

Reduced

productivity

Employee

downtime

Time and resources consumed

Trying to isolate problem

Incomplete

transactions

Lost revenueCustomerabandonment


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Why Monitor from the Rea l

User Perspec tive?

RT (Payload / Bandwidth) + (AppTurns * RTT) + Cs+ Cc

RT Response t ime of the transac t ion in seco nds

PayloadThe amount o f info rmation (bytes) tha t must be de live red tothe user

BandwidthMinimal bandwidth across all network links between the usera nd the d a ta c enter

AppTurns Number of user and web site interac tions needed togenerate a user-level system response to a transac tion

RTTRound-trip-time between the user and the data center

Cs

Tota l processing time required by the data center consisting

of web servers, ap plication servers and da tabase servers

Cc Total proc essing time required by the users PC

Calculating end user response time is not

practical


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Payload Varies grea tly transac tion to transac tion 3rd pa rty or c ac hed c ontent Non-page content like AJAX, Flash, Silverlight

Bandwidth Varies greatly from user to user

Varies from moment to moment

AppTurns Varies grea tly transac tion to transac tion 3rd pa rty or c ac hed c ontent Non-page content like AJAX, Flash

RTT Varies from moment to moment

Cs

Varies from transac tion to transac tion

Dynamic da ta c enterwha t pa th will the transac tion take? Difficult to instrument app lica tions, esp . 3rd pa rty c od e

Cc Varies from user to user, moment to moment

Imp ac ted by last mile c ond itions

Parameter

How Rea l-Time Apps Dera il RT

Calculations

Limitations


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Measuring or Estimating Rea l User

Response Time (RT)

Empiric al / Approximate Direc t

Passive SnifferInsta lled Ag ent

orDynamic Injec tion

Active Synthetic Monito ring (not app licab le)

Measuring RT by

listening-in and

not adding load

Measuring RT of

artificially createdtransactions

RT derived through

measurement ofsurrogate parameters

Measuring RT directlyat the browser


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Direc t Measurement a t Browser Only Viab le Approac h for Ajax

Apps

Last m ile c onnec tivity issues

User side bandwid th or c ac hing

Cha tty protoc ol (e.g., XML)

Content delivery network

User side resourc e limita tions impac ts Web 2.0 fea tures

Javascript

Med ia p layers (e.g., Flash, Silve rlight)

Mash-up , SOA & SaaS mask performanc e issues

Computing in the Cloud beyond the d a tac enter

Web applications do not come together until

the users browser


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Measuring or Estimating Rea l

User Response Time (RT)

Empiric al Direc t

Passive SnifferInsta lled Ag ent

orDynamic Injec tion

Active Synthetic Monito ring (not app lic ab le)

Measuring RT by

listening-in and

not adding load

Measuring RT

of

artificially created

transactions

RT derived through

measurement ofsurrogate parameters

Measuring RT directlyat the browser

Dynamically injects probe onto

browser via Web server or ADC Non-intrusive

No agent download

No source code changes

Measure RT & errors

Applicable to all customer-facing

or enterprise applications

Dynamic Agent

Download monitoring agent to PC

Difficult & expensive to implement Convince users to download

Maintain agents

Potential compatibility issues

Measure RT, errors & desktop

statistics

Only suitable for PCs under ITs

direct control

Installed Agent


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Beyond Monitoring End -to-End

Management

Web App DB

Management

Server + DB

Externa l App

SaaS

Tier Time Detail

Web

App

DB

Ext 1

Ext 2

Tota l

HTML, AJAX, Flash,Silverlight


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Meaningful, Correla ted &

Ac tionab le Da taRT (as experienced by the end-

user)

Affec ted Partys IP Ad dress and URL

Network Latency

Parsing Time

Objects Per Page

Ob jec t Response Time

Error or Abort Rate

Base Page Response Time

Response Time at Web , Application &Database Tier

Server Responsible at Each Tier

Server Parameters: CPU utilization, Memory,I/ O etc .

Web Service Calls

Method Call Tree

SQL Queries

Everythingmeasured fromthe rea l user s

perspective

Insight into app lic a tion

Correla ted ac ross a ll tiers ofnetwork & infrastruc ture


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Rea l Time, End User Experienc eDriven Prob lem Resolution

Detect Problem Based on RT

Assess Impact

Prioritize Issues

Outside or Inside?

Client or Network? Front or Back End?

Identify

Individual User

Identify

Individual IP

Which Page,

Object, WebService, Server?

Which Object

and Server?

Trace Call Stack

Method Call orSQL Query?

Outside Inside

Client Network Front End Back End

Solve The Problem


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Performanc e Measurement

Based on Rea l Users


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Quick Triage

Directly relate real user RT to IT issues Not impacted by infrastructure configuration

Accommodate 3rd party content, SOA etc.

Focus resources on fixing the problem instead of

reproducing the problem or pointing fingers


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Operate

OptimizeRequirements

Development Phase Production Phase

Discover & fix performance bottle-necks

under load prior to rollout

Real-time detection & mitigation of

performance issues

Tuning Web Ap p .

Performanc e Using Rea l Data

DeployBuild

Design


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Complexity Crea tes a

Spec trum of User Experienc es

Web App DB

Externa l App

HTML

AJAXFlash,

Silverlight

Response Time

#ofOccurrence


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How to Report App . Perf. to

Business Owners

One approach: Application Performance Index (Apdex)

Standardized method for reporting app. perf. as defined by anallianc e o f companies and users (www.apdex.org)

Reduced myriad of perf. metrics into a 0-to-1 sc ale (0=no user

satisfied, 1=all users satisfied)

APDEXT =Num. Satisfied Users + Num. Tolerating Users

Total Num. Users

=4T
http://www.apdex.org/http://www.apdex.org/


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Aligning App Perf to Business

Goals
http://www.apdex.org/using.html


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Sample Ap dex Report


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Requirements of a

Comprehensive Tool

Provides visibility into

browser-level

performance, includingRIAs

Detect performance

problems in real time to

minimize impact

Report on business

impact of performance

problems Optimize application

performance with

historical trending and

analysis

Detect

Isolate problems by

tagging and tracing

transactions throughinternal and 3rd party

J2EE and .NET services

Visibility into problem

servers, services,

method calls and SQLqueries

Isolate Optimize

Web App DB


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Thank you!

For more information:

Hon Wong

CEOSymp honiq Corpora tion(650) 213-8889 x101

hon@symphoniq .c om

www.symphoniq.com

Documents

Optimizing Web20 App Performance