web overview the birth of web: 1989 now web is about everything – business (hr systems, e.g. nuhr)...

Web Overview

• The birth of Web: 1989• Now Web is about everything

– Business (HR systems, e.g. NUHR)– Online Shopping (Amazon), Banking (Citibank,

Chase)– Communications (Gmail, Facebook)

• Become mission-critical – Performance– Security

Web 2.0

• Web 1.0– Basic HTML + Images

• What is Web 2.0?– No one really gives a clear definition– Features

• AJAX ( Asynchronous JavaScript and XML)• DOM (Document Object Model) • Flash• CSS (Cascading Style Sheets)• User involvement: Wiki, Blog, Social Networks

Web 2.0 Basics - JavaScript

• JavaScript– A scripting language with C/C++ like grammar– Dynamic, weakly typed language

• Eval()• No need to claim the object types

• Web 2.0 websites are JavaScript heavy– Google Maps (510KB)– Google Calendar (152KB)– Facebook (558KB)

DOM (Document Object Model)

• One of the first JavaScript/DOM heavy apps: Gmail• DOM Event API: Keyboard and mouse events• DOM CSS API

<html> <head> <title>Sample Document</title> </head> <body> <h1>An HTML Document</h1> <p>This is a <i>simple</i> document.</html>

AJAX ( Asynchronous JavaScript and XML)

• Foundation of popular web apps: Google Map, Gmail, Facebook, etc.

• Can transfer any object between browser Web server, e.g. XML or JSON (JavaScript Object Notation)

req = new XMLHttpRequest();

function callback () { … }function handler () { if (req.readyState == 4 && req.status == 200) { callback(req.responseText); }}

req.onreadystatechange = handler;req.open(“GET”, url, true);req.send(null);

XMLHttpRequest

register a callback function to be asynchronous

enable JavaScript visit the url directly

response can be either plain text or XML

6

WebProphet: Automating Performance Prediction for Web

Services

Zhichun Li, Ming Zhang, Zhaosheng Zhu, Yan Chen, Albert Greenberg and Yi-min Wang

Northwestern UniversityMicrosoft Research

7

Large-scale Web Services• Most large-scale online services today are web-

based– Web search, map, Webmail, calendar, online stores, etc.

• Provided by Online Service Providers (OSPs)– MSN, Google, Yahoo, Amazon, etc.

• Hosted by multiple data-centers around the world• More and more complex

– Yahoo Maps: 110 embedded objects, complex object dependencies and 670KB JavaScript

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• Amazon: 1% sale loss at the cost of 100ms extra delay• Google found 500 ms extra delay reduce revenues by up

to 20%

Performance Is Important

OSP A

OSP B

Revenue

Revenue

SLOW!Need a tool to understand and improve the user perceived

performance.

9

Large Web Services Are Complex

BrowserBrowser

Backend DCs

Frontend DCs

OSP Internal Network

DNS

Internet

Complex UI large browser delayPoor object dependency more RTTs(online map needs 40~60 http objects)

Potential Performance Problems

Complex DNS redirection long dns query(CNAME)Different servers more dns queries

RTTPacket loss interact with TCP

Overload Long response time

Overload Long response time for dynamic contents

RTTPacket loss

Need a tool to diagnose why slow? and where is the

bottleneck?

Performance Prediction Problem

• Many ways can be used for performance optimization. However, cannot try them one by one, huge cost!

• What the performance will be under hypothetical optimization strategies?

• How to quickly evaluate the predicted performance?

Performance ???Optimization

11

Outline

• Motivation• Design• Dependency Extraction• Performance Prediction• Implementation• Evaluation• Conclusion

Client Side Performance Prediction

• Provider-based techniques – Hard to consider multiple data sources– Object dependencies– Page rendering time

Internet

CDN

Data Center

Data Center

The Page Load Time Decomposition

Page Load time

ObjectDependency

Client Delay Net Delay Server Delay

DNS Delay Data Transfer

RTT Packet loss

Load time of Object i

TCP 3-WAY

System Architecture

Measurement Engine

Dependency Extractor

Performance Predictor

New Scenarios

Results

PDGs

15

Outline


What are dependencies?

• The embedded objects in an HTML page• Object requests generated by JavaScript

depend on the corresponding .JS files• External CSS and JavaScript files blocks the

other embedded objects in the HTML page• Event triggers, such as when image B trigger

“onload” event, then image A will be load by JavaScript

Dependency Definitions

• Descendant(X): objects that depend on X • Ancestor(X): objects that X depends on• Parent(X): The objects that X directly depends

on. Direct means can be the last among ancestors

• Based on parent relationship build PDG (parental dependency graph)

Discover Ancestors and Descendants

• We discover the descendant(X) sets by using time perturbation through HTTP proxy.

Extract non-stream parents

• Stream VS. Non-stream– HTML is stream objects and other types of objects

are non-stream

• Non-stream parent extraction

A B

C

D

X

Y Z

Descendant(A)={B,D}Descendant(B)={D}

Extract stream parents

• 1) Load the HTML page very slow

• 2) Delay other known non-stream parents

X

Y Z

Offset(Z)

X

Y Z

Extract stream parents

• 1) Load the HTML page very slow

• 2) Delay other known non-stream parents

X

Y Z

Offset(Z)

X

Y Z

Offset2(Z)

22

Outline


Performance Prediction Procedure

Extract Object timing

information

Annotate client delay

Adjust each of object according to new scenario

Simulate the page load

process

Packettrace

PDGNew

ScenarioPDG

Object Timing Info

• Basic object timing info

• Adding client delay info

X

Parent(X)Client delay

DNS

TCP

HTTP

DNS lookup time

TCP handshaking time

Response time

Reply transfer timeRequest transfer time

Adjust Object Timing Info

• Adjust DNS lookup time directly• Server response time: change the response

time• RTT:

m * ΔRTT n * ΔRTT

ΔRTT

Simulating Page Load Process I• Browser behaviors

HTTP time

(c) without DNS and TCP time

(b) with TCP time

(a) with both DNS and TCP time

HTTP timeTCP time

Client Delay

HTTP timeTCP timeDNStime

Tr HTTP request ready

Tp last parent

available Tf Tl

Tf Tl

Tr

Tf Tl

TrTp

Tp

HTTP time

(d) with TCP waiting time

Tf TlTp

TCP waiting time

Tr

Client Delay

Client Delay

Client Delay

Simulating Page Load Process II

• Page load process– Find the earliest candidate C from

CandidateQueue– Load C according to the conditions in the pervious

slide– Find new candidates whose parents are all

available– Adjust timings of new candidates– Insert new candidates into CanidateQueue

28

Outline


29

WebProphet Framework

Browser

Control plug-in

Web robotScripting API

Application transaction

script snippet

Pcap trace loggerAgent network

Results

New scenario input

PDGs

Web Agent

Web Proxy

Dependency Extractor

Annotate object timing info

Page simulator

Trace Analyzer

Performance Predictor

TracesThe whole system is about

12,000 lines of code

Dependency Extraction Results

• Google and Yahoo Search

• Validation: manual code analysis

HTML

9 Images

HTML

CSS Image

Image Image Image Image

Javascript

Google Yahoo

Dependency Extraction Results• Google and Yahoo Maps

• Validation: Create fake pages with the same PDGs and validate the fake pages

#HTML=1

#JS=1 #HTML=1,#JS=1,#IMG=17

#JS=1#JS=1,#IMG=28#JS=1 #JS=3

#IMG=1

#IMG=8

#HTML=1

#CSS=2

#JS=1

#JS=1

#JS=1

#JS=1

#IMG=1

#IMG=2 #IMG=2

#JS=5,#IMG=3 #JS=1 #JS=1

#IMG=1 #IMG=4 #JS=1 #JS=1 #JS=1 #HTML=2,#IMG=65

#IMG=1 #IMG=10 #HTML=1 #IMG=1

#IMG=1

Google

Yahoo

Predication Accuracy

• Evaluate both median and 95-percentile• Control experiment

– 50% cases with predication error less than 6.1%– 90% cases with predication error less than 16.2%

• Planetlab experiment– Predication error of median less than 6.1%– Predication error of 95-percentile less than 10.7%

Usage Scenarios

• Analyze how to improve Yahoo Maps– Only want to optimize a small number of objects– Use a greedy based search– Evaluate 2,176 hypothetical scenarios, find that

• Move 5 objects to CDN: 14.8%• Reduce client delays of 14 objects to half: 26.6%• Combine both: 40.1%

34

Outline


Conclusions

• Develop a novel technique to extract the object dependencies of complex web pages

• Implement a simple but yet effective model to simulate the page load process

• Apply Webprophet to Yahoo Map to show that it can be useful for performance optimization

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