ICDCS’07, Toronto, Canada1 SCAP: Smart Caching in Wireless Access Points to Improve P2P Streaming Enhua Tan 1, Lei Guo 1, Songqing Chen 2, Xiaodong Zhang.

ICDCS’07, Toronto, Canada 1

SCAP: Smart Caching in Wireless Access Points to Improve P2P

Streaming

Enhua Tan1, Lei Guo1, Songqing Chen2, Xiaodong Zhang1

1The Ohio State University2George Mason University

2

Background

Wireless access to Internet is pervasive: On campus, in offices, at home, and public utilities Most are supported by Wireless LANs

Peer-to-Peer applications are widely used: Streaming: PPLive, Joost, etc … VoIP: Skype, etc … Large file distribution: BitTorrent, etc …

Our Focus: Interaction between wireless users and P2P streaming applications

3

Wired/wireless Communications

WLAN

Internet

Access Point (AP)

Wireless users

Wired users

4

P2P Streaming for Wired/wireless Users:Workflow

WLAN

Internet

Source Peer

Wireless Peer

Viewing Peer

Access Point

5

P2P Streaming for Wired/wireless Users: Problems

WLAN

InternetDownstream traffic for other wireless users AFFECTED

Generating upstream traffic

Streaming quality degradedWireless Peer

(Relay/Viewing)

Viewing PeerStreaming content

Other packets

Source Peer

6

Problem Summary

Peers in WLAN may relay streaming content by uploading a lot of traffic:

Congest the WLAN due to channel competitions Provide low quality of service to the Internet peers

Downstreams have lower priority due to upstreams

Extra upstream traffic: further increase the number of transmission errors increase the cost of contention window back-off

Major problem source: upstream relay trafficupstream relay traffic

Can we minimize upstream traffic with low overhead? to improve WLAN throughput to improve service quality for Internet peers

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WLAN

Internet

Wireless Peer

Viewing Peer

Access Point

The same content is transferred twice in

the WLAN! Duplicated traffic

P2P Streaming for Wired/wireless Users:Workflow

Source Peer

8

Contributions

Our measurements show that > 75% upstream traffic is duplicated with the downstream traffic for three representative applications

SCAP: Smart Caching in the Access Point for minimizing upstream traffic: design & prototype implementation

Evaluation results show SCAP can improve the throughput of the WLAN by up to 88%:

SCAP also reduces the delay to Internet peers

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Outline

Problem Summary and Contributions Measurement & Analysis of P2P Streaming

Traffic SCAP Design & Implementation Evaluation Summary

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Measurement & Analysis of P2P Streaming Traffic

Aim to answer two questions: How much duplicated traffic in practice? How much overhead in identifying such

duplications?

Measurement: Collect traces of three representative P2P live

streaming applications: PPLive, ESM, and TVAnts In LAN (100Mbps) and WLAN (802.11b)

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Workload Statistics

Downstream throughput is typically 300~400Kbps Upstream traffic to downstream traffic:

Can be as large as 10 times for PPLive due to its popularity Between 2 to 4 times for TVAnts Not too much for ESM

PPLive and ESM: most in TCP TVAnts: 74% in UDP for WLAN

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Downstream packetDownstream packetFIFO bufferFIFO buffer

Duplication Detection Methods:Fixed Hashing

Offline workload analysis: Fixed Hashing (FH)

Compute only 1 fingerprint (hash value) for a downstream packet; store this fingerprint in a hash table, and cached the packet in FIFO buffer

For each upstream packet, also compute the fingerprint, and look it up in the hash table to locate the duplicated downstream packet; If found the same fingerprint, do further byte-to-byte comparison

Downstreampacket

fingerprinthash table

Upstreampacket

Upstreampacket fingerprint

Lookup

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Duplication Detection Methods:Rabin Fingerprinting

Rabin Fingerprinting (RF) A unique hash function: produce fingerprints for a

continuous data stream quickly (NSDI’07 BitTyrant) We scan the whole packet and only store fingerprints

ending with 8 zeros over 64 bytes content averagely 5 fingerprints for a 1400 bytes packet (1/28)

FIFO Buffer: stores latest 50,000 downstream packets

Buffer + hash table: need about 75MB memory totally

14PPL-

LAN

PPL-

WL

TVA-

LAN

TVA-

WL

ESM-

LAN

ESM-

WL

0

50

100

150

200

250

300

350

400

450

RF

FH

RF-BufAll

Thr

ough

put (

Mbp

s)

PPL-

LAN

PPL-

WL

TVA-

LAN

TVA-

WL

ESM-

LAN

ESM-

WL

0

10

20

30

40

50

60

70

80

90

100

RF

FH

RF-BufAll

Dup

licat

ion

ratio

(%)

RF can detect more duplications than FH

All the duplication ratios are larger than 75%

Offline analysis processing throughput of RF is less than FH:

Still large enough (> 90Mbps) for process P2P streaming (400 Kbps)

Dup Ratio & Tput

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Duplication Beginning Offset

FH can only detect the duplication when the offsets for up/downstream packets are the same (no re-packetizing)

ESM does not have any offset differences FH performs well

TVAnts has a lot of re-packetizing FH performs the worst

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Forwarding Delay

PPLive and TVAnts: most upstream packets forwarded in 200 seconds

<20 seconds for 70% ESM: within 10 ms Implies the downstream

buffer can be quite small

200seconds

200seconds

10 ms

10seconds

20seconds

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Outline

Problem Summary and Contributions Measurement & Analysis of P2P Streaming

Traffic SCAP Design & Implementation Evaluation Summary

18

SCAP (Smart Caching in Access Points) Overview

WLAN

Internet

Downstreams buffer

Metadata upstream packet

(If duplications found in downstream buffer)

Relay/Viewing Peer

Access Point

Original upstream packet

Downstream buffer

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Design Issues

Buffer size: Need 7.5MB for storing recent 200 seconds traffic (in

300Kbps rate), which is affordable for a wireless station But AP will need to buffer for multiple stations:

AP should dynamically adjust the buffer space for each station according to its duplication ratios in order to achieve highest traffic reduction with limited buffer space

Buffer synchronization between AP and station: If a metadata upstream packet cannot be reassembled on

AP due to a cache miss, TCP flow will be stalled Wireless station caches several copies of recent sent upstream

packets and resends the uncompressed packet when needed

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Prototype Implementation

Modified HostAP driver in Linux kernel 2.6.16 for the AP and stations

Wireless card is based on Intersil Prism 2.5 chipset (802.11b)

Identification of the downstream packet For AP to locate the packet in decompressing the

upstream packet Cannot use Sequence Control field (2 bytes)

because it is filled by the firmware Have to use the first fingerprint value (8 bytes)

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Outline

Problem Summary and Contributions Measurement & Analysis of P2P Streaming

Traffic SCAP Overview Design & Implementation Evaluation Summary

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Performance Evaluation: LAN Experiment

Station first receives a file from a server, then sends it back RF: little overhead for the downstream throughput (1.5%

decrease), and 88% improvement for the upstream throughput FH: cannot have any improvement due to constant TCP re-

packetizing

1MB 7MB 70MB 140MB0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Orig

RF

FH

Transfer File Size

Dow

nstr

eam

Thr

ough

put(

Mbp

s)

1MB 7MB 70MB 140MB0

1

2

3

4

5

6

7

8

9

Orig

RF

FH

Transfer File SizeU

pstr

eam

Thr

ough

put (

Mbp

s)

4.50

4.43 Mbps

4.7

8.9 Mbps

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Performance Evaluation:Internet Experiment

Evaluate PPLive, TVAnts, and ESM Run the applications in a VMWare-based Windows XP

guest OS for HostAP driver to work Measurement methods:

Because P2P Streaming is a Constant Bit Rate stream: Upstream throughput will not change even if we reduces its traffic Running iperf on another wireless station to observe the impact to

WLAN TCP throughput Running Ping to observe the impact to response time Run multiple trials to get comparable P2P downstream

throughput for comparison Each trial runs for 600 seconds

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Internet Experiment:Evaluation Results

RF/FH performs best for TVAnts since it has the largest volume of upstream traffic:

Increases TCP throughput by 0.95 Mbps (54% of upstream traffic)

Decrease Ping round-trip time by 83 ms (-26%)

Also performs well for PPLive/ESM

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Summary

With the increasing popularity of P2P streaming applications and pervasive deployment of 802.11 WLANs, more peers will be connected by wireless

We study the impact of wireless peers to the performance of wireless and Internet users

Without a proper control of P2P traffic, the performance of both parties can be significantly affected

We designed and implemented SCAP (Smart Caching in Access Points) in order to reduce the upstream traffic for P2P live streaming applications

Our prototype based evaluation shows the effectiveness of SCAP:

SCAP improves the throughput of the WLAN by up to 88% SCAP reduces the response delay to Internet peers as well

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Thank you!

Enhua Tan: [email protected]://www.cse.ohio-state.edu/hpcs/

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SCAP (Smart Caching in Access Points) – Basic Idea

AP stores downstream data in buffer (1) Station stores downstream data in buffer (2) Compare upstream packet (3) with (2), upload difference (4) AP will assemble upstream packet with data in (1) to the

Internet

Access Point (AP) Wireless Station

(1)

(4)

(2)

(3)

Incoming

Outgoing

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AccessPoint

WirelessStation

Router

HostAP Driver

Downstream Buffer

Downstream Buffer

P2P Streaming Application

Duplication Detection;

CompressingLookup

Decompressing

Downstream packet

Uptream packet Compressedupstream packet

Workflow of SCAP

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Rabin Fingerprinting

mmm atatatA ...)( 2

21

1

)(mod)()( tPtAARF

),...,,( 21 maaaA Rabin Fingerprinting (RF)

can produce fingerprints for a continuous data stream quickly:

Advance the fingerprint only requires an addition, a multiplication, and a mask

Lack of this property for other hash functions like MD5/SHA (and they are also more complex)

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Some Related Work

XORs in the Air: Practical Wireless Network Coding (Sigcomm’06)

Utilizing the broadcasting nature of wireless networks to improve throughput of multi-hop network (instead of application characteristics)

Our scheme is utilizing the traffic pattern of P2P applications

A Protocol-Independent Technique for Eliminating Redundant Network Traffic (Sigcomm’00)

reduces redundant traffic using Rabin Fingerprinting A Low-bandwidth Network File System (SOSP’01)

Exploits similarities between different versions of a file to reduce update traffic