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TOWARDS UNDERSTANDING DEVELOPING WORLD TRAFFIC. Sunghwan Ihm (Princeton) KyoungSoo Park (KAIST) Vivek S. Pai (Princeton). IMPROVING NETWORK ACCESS IN THE DEVELOPING WORLD. Internet access is a scarce commodity in the developing world: expensive / slow

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towards understanding developing world traffic

TOWARDS UNDERSTANDING DEVELOPING WORLD TRAFFIC

Sunghwan Ihm (Princeton)

KyoungSoo Park (KAIST)

Vivek S. Pai (Princeton)

improving network access in the developing world
IMPROVING NETWORK ACCESS IN THE DEVELOPING WORLD
  • Internet access is a scarce commodity in the developing world: expensive / slow
  • Our focus: improving performance of connected network access
  • Non-focus: providing/extending connectivity (e.g., DTN, WiLDNet)

2

Sunghwan Ihm, Princeton University

possible options
POSSIBLE OPTIONS

Web proxy caching

Whole objects

Single endpoint (local)

Designated cacheable traffic only

WAN acceleration

Packet-level caching

Mostly for enterprise

Two (or more) endpoints, coordinated

Effective in first world

3

Sunghwan Ihm, Princeton University

developing world questions
DEVELOPING WORLD QUESTIONS
  • How effective are these approaches?
    • Systems designed for first-world use
    • Most traffic studies small, first-world focused
    • How similar is developing region traffic?
  • Any new opportunities to exploit?
    • Differences in traffic
    • Differences in cost/tradeoffs
    • System design issues

4

Sunghwan Ihm, Princeton University

understanding developing world traffic
UNDERSTANDING DEVELOPING WORLD TRAFFIC

Goal

Shape system design by better understanding the traffic optimization opportunities

Requirements

Large-scale, content-focused analysis

5

Sunghwan Ihm, Princeton University

prior traffic analysis work
PRIOR TRAFFIC ANALYSIS WORK
  • Large scale traffic analysis
    • Internet Study 2007, 2008/2009 by ipoque
    • One million users
    • High-level characteristics via DPI
    • First-world focus
  • Developing world traffic analysis
    • Du et al. WWW’06, Johnson et al. NSDR’10
    • Proxy-level analysis from kiosk, Internet cafes, and community centers

6

Sunghwan Ihm, Princeton University

our approach
OUR APPROACH
  • Combine best features
    • Large-scale and content-focused
    • First world and developing world
  • Use traffic from CoDeeN content distribution network (CDN)
    • Global proxy (500+ PlanetLab nodes)
    • Running since 2003
    • 30+ million requests per day

7

Sunghwan Ihm, Princeton University

what to analyze
WHAT TO ANALYZE?

Traffic profile

Caching opportunities

User behavior

8

Sunghwan Ihm, Princeton University

data collection

WAN

Browser

Cache

Local

Proxy

Cache

CoDeeN

Cache

DATA COLLECTION

Origin

Web Server

User

  • Assume local proxy caches
  • Focus on cache misses only
  • Capture full content

9

9

Sunghwan Ihm, Princeton University

data set
DATA SET
  • Duration: 1 week (March 25-31, 2010)
  • # Requests: 157 Million
  • Volume: 3 TeraBytes
  • # Clients (unique IPs): 348 K
  • # Countries/Regions: 190
    • /8 networks coverage: 61.3%
    • /16 networks coverage: 24.1%

10

Sunghwan Ihm, Princeton University

top countries
TOP COUNTRIES

Requests %

Bytes %

Clients %

SA

PL

CN

Etc.

Etc.

Etc.

PL

CN

CN

DE

PL

US

SA

DE

AE

US

RU

SA

PL (Poland)

DE (Germany)

CN (China)

US (United States)

SA (Saudi Arabia)

RU (Russian Federation)

11

Etc.(185 Countries)

AE (United Arab Emirates)

oecd vs devreg
OECD VS. DEVREG
  • OECD: the first world
    • 27 high-income economies from OECD member countries
    • 25% of total traffic
  • DevReg: the developing world
    • The remaining 163 countries and 3 OECD members: Mexico, Poland, and Turkey
    • 75% of total traffic

12

Sunghwan Ihm, Princeton University

analysis 1 traffic profile
ANALYSIS #1: TRAFFIC PROFILE
  • Conjecture:

DevReg users visit low-bandwidth Web pages (small objects and text-heavy)

  • We often hear a variant of

“Offline Wikipedia content suffices for developing world users”

13

Sunghwan Ihm, Princeton University

object size
OBJECT SIZE
  • Small: median 3KB vs. 5KB
  • Large: similar demand/profile

16KB

14

Sunghwan Ihm, Princeton University

text and images
TEXT AND IMAGES
  • DevReg has a higher fraction of images
  • Exact opposite of bandwidth conjecture

15

Sunghwan Ihm, Princeton University

video and audio
VIDEO AND AUDIO
  • DevReg: higher fraction of video & audio
  • Music videos and MP3 songs

16

Sunghwan Ihm, Princeton University

application flash
APPLICATION (FLASH)
  • DevReg has a higher fraction of application traffic
  • Median near 7%

17

Sunghwan Ihm, Princeton University

analysis 1 summary
ANALYSIS #1 SUMMARY
  • Some evidence that DevReg-visited sites have smaller objects, but
  • DevReg users visit large pages as well, and
  • DevReg users seek a higher fraction of rich content than OECD users

18

Sunghwan Ihm, Princeton University

analysis 2 caching opportunity
ANALYSIS #2: CACHING OPPORTUNITY
  • Conjecture: little gain from larger caches
    • Some analysis suggests 1GB sufficient
    • Typical cache size < 20GB
    • Object-based caching

19

Sunghwan Ihm, Princeton University

content based chunk caching

A

B

C

D

E

CONTENT-BASED CHUNK CACHING
  • Split content into chunks
    • Name chunks by content (SHA-1 hash)
    • Cache chunks instead of objects
  • Fetch content, send only modified chunks
    • Two endpoints needed
    • Applies to “uncacheable” content

20

Sunghwan Ihm, Princeton University

overall redundancy
OVERALL REDUNDANCY
  • 40% @ 64 KB: objects or parts of large object
  • 60% @ 1 KB: parts of text pages
  • 65% @ 128 bytes: paragraphs or sentences

21

Sunghwan Ihm, Princeton University

cache behavior simulation
CACHE BEHAVIOR SIMULATION
  • Simulate one week’s traffic
    • Cache misses only
    • LRU cache replacement policy
  • Determine size for near-ideal hit rate
    • Calculate byte hit ratio (BHR)
    • Vary storage size (from 10MB to max)
  • Results for US, China, and Brazil

22

Sunghwan Ihm, Princeton University

analysis 2 summary
ANALYSIS #2 SUMMARY
  • Chunk caching useful
    • Reduces WAN (cache miss) traffic
    • Complements existing Web proxies
  • Larger caches useful
    • Useful reduction in miss rate
    • Cheap compared to bandwidth costs

26

Sunghwan Ihm, Princeton University

analysis 3 user behavior
ANALYSIS #3: USER BEHAVIOR
  • Conjecture: as first-world Web pages get larger, DevReg users suffer delays
  • Mechanism: observe aborted transfers
    • Intentional termination
    • Automatic when browsing away
  • Abort = users bored or downloads slow

27

Sunghwan Ihm, Princeton University

cancelled object size c cdf
CANCELLED OBJECT SIZEC-CDF
  • Cancelled objects larger than normal (red)
  • Complete objects (green) much larger than actual download (blue)
  • Most downloads less than 10MB

28

Sunghwan Ihm, Princeton University

cancelled transfer volume
CANCELLED TRANSFER VOLUME
  • 17% of transfers are terminated early
  • Due to the early termination, 25% of actual traffic
  • If fully downloaded, would have been 80% of all bytes
    • Overall traffic increase of 375%

29

Sunghwan Ihm, Princeton University

cancelled content types
CANCELLED CONTENT TYPES
  • Most canceled responses were text
  • Most bytes from video/audio/application

30

Sunghwan Ihm, Princeton University

cancelled requests cdf
% CANCELLED REQUESTS CDF
  • OECD cancel more often than DevReg
    • Median almost double

31

Sunghwan Ihm, Princeton University

analysis 3 summary
ANALYSIS #3 SUMMARY
  • Many transactions aborted
  • Previewing video files
    • Content-based caching is effective
  • OECD users less patient than DevReg
    • Cheap bandwidth = more sampling?

32

Sunghwan Ihm, Princeton University

conclusions
First glimpse at CoDeeN traffic

Large-scale, content-focused analysis

OECD and developing world

Many DevReg assumptions are false

In fact, strong desire for rich content, and

Patient despite slow connections

Systems implications

Chunk caching worth more exploration

Larger caches very useful

CONCLUSIONS

33

Sunghwan Ihm, Princeton University

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