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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


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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)

slide9

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.