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PPWEB: A Peer-to-Peer Approach for Web Surfing On the Go. Ling-Jyh Chen, Ting-Kai Huang Institute of Information Science, Academia Sinica, Taiwan Guang Yang Nokia Research Center, Palo Alto, US. Motivation. Web surfing is part of our life.

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Ppweb a peer to peer approach for web surfing on the go

PPWEB:A Peer-to-Peer Approach forWeb Surfing On the Go

Ling-Jyh Chen, Ting-Kai Huang

Institute of Information Science, Academia Sinica, Taiwan

Guang Yang

Nokia Research Center, Palo Alto, US


Motivation
Motivation

  • Web surfing is part of our life.

  • How can we surf the Web when we cannot directly access the web pages?

    • No connections

    • Censorship

  • Mobile devices are hugely popular.

  • How can we browse the Web when we are on the go?

    • Cellular

    • Wi-Fi Hotspots


Previous solutions
Previous Solutions

  • Offline-based approaches

    • Gnu Wget

    • Wwwoffle

    • Well-known web browsers

  • Cache-based approaches

    • Push based (Aalto ‘04, Costa-Montenegro ‘02, Spangler ‘97)

    • Pull based (Jiang ’98a, Jiang ’98b, Padmanabhan ‘96)

  • Infostation-based approaches

    • Mobile Hotspots (Ho ‘04)

    • Thedu (Balasubramanian ‘07)


Previous solutions drawbacks
Previous Solutions (Drawbacks)

  • Offline-based

    • manually download web documents

    • limited number of web pages

  • Cache-based

    • Tremendous storage overhead

  • You still need a data plan to surf.

  • Infostation-based

    • Dedicated Infostations needed

    • Single point of failure


Assumptions we make
Assumptions We Make

  • All peers collaborate.

  • All peers have local connectivity

    • WiFi, Bluetooth, etc.

  • All peers are mobile.

  • Some peers have Internet access.


What we propose scenario 1

HTTP

What We Propose: Scenario 1

Internet

Gateway Peer:

A peer who can access the Internet directly


What we propose scenario 2a
What We Propose: Scenario 2a

Gateway Peer (B)

Vanilla Peer (A):

Peer that cannot access Internet directly


What we propose scenario 2b
What We Propose: Scenario 2b

Vanilla Peer (A)

Vanilla Peer (B)


Ppweb

Y

N

Y

N

Y

N

N

N

Y

B gets A’s request

B is a GP

B and A are

connected

B has the requested

web content

Direct forwarding

The request has been

relayed H times

B and A are

connected

Collaborative

forwarding

Y

Indirect

Forwarding

Do nothing

Request

Forwarding


Direct forwarding vs indirect forwarding
Direct Forwarding vs. Indirect Forwarding

  • B has complete content =>Direct Forwarding algorithm

  • B may only have partial content =>Indirect Forwarding algorithm

    • Further passing the request message using Request Forwardingalgorithm


Cooperative forwarding algorithm
Cooperative Forwarding Algorithm

  • Increase the packet delivery ratio and decrease the request response time

  • HEC-PF

    • Hybrid Erasure Coding Algorithm (H-EC)

    • Probabilistic Forwarding Algorithm

  • Erasure codes increase error tolerance.

  • Extra caching increases hit ratio in the future (esp. for popular pages).


Evaluations
Evaluations

  • Evaluate the performance of PPWEB scheme against Mobile Hotspots scheme

    • Service ratio and traffic overhead

  • DTNSIM: Java-based simulator

  • Real wireless traces

    • UCSD (campus trace)

    • iMote (Infocom ‘05)



Parameter settings
Parameter Settings

  • Number of VPs:

    • 20% of the other peers

  • Number of requests:

    • first 10% of simulation time with a Poisson rate of 1800 sec/request.

  • The HTTP requests:

    • top 500 requested web pages,

    • campus proxy server of NTU, Apr.-Sept. 2006.


Scenario 1 ucsd
Scenario 1: UCSD

γ= 20%

γ= 60%


Scenario 2 imote
Scenario 2: iMote

γ= 20%

γ= 60%


Traffic overhead
Traffic Overhead

Replication factor of erasure coding = 2

Aggressive forwarding phase of the HEC-PF: make one more copy

The upper bound of the traffic overhead : 2*2=4


Summary
Summary

  • PPWEB is a peer-to-peer solution to enable mobile web surfing.

  • No constant Internet access is required.

  • No dedicated servers are required.

  • It implements a Collaborative Forwardingalgorithm that takes advantage of opportunistic encounters.



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