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Transcript 
Wireless Internet
Performance Research
Carey Williamson
iCORE Professor
Department of Computer Science
University of Calgary
www.cpsc.ucalgary.ca/~carey
[email protected]
1
Internet Protocol Stack
r
Application: supporting network
applications and end-user services
m
r
Transport: end to end data transfer
m
r
IPv4, IPv6, BGP, RIP, routing protocols
Data Link: hop by hop frames,
channel access, flow/error control
m
r
TCP, UDP
Network: routing of datagrams
from source to destination
m
r
FTP, SMTP, HTTP, DNS, NTP
PPP, Ethernet, IEEE 802.11b
Application
Transport
Network
Data Link
Physical
001101011...
Physical: raw transmission of bits
2
The Wireless Web
r The emergence and convergence of these
technologies enable the “wireless Web”
m
m
m
the wireless classroom
the wireless workplace
the wireless home
r My iCORE mandate: design, build, test, and
evaluate wireless Web infrastructures
r Holy grail: “anything, anytime, anywhere”
access to information
(when we want it, of course!)
3
Research Interests
r Wireless Internet Technologies
r MAC Protocol Design
r Network Traffic Measurement
r Workload Characterization
r Traffic Modeling
r Network Simulation
r Web Performance
4
Wireless Internet Technologies
r Mobile devices (e.g., notebooks, laptops,
PDAs, cell phones, wearable computers)
r Wireless network access
m
m
m
Bluetooth (1 Mbps, up to 3 meters)
IEEE 802.11b (11 Mbps, up to 100 meters)
IEEE 802.11a (55 Mbps, up to 20 meters)
r Operating modes:
m Infrastructure mode (access point)
m Ad hoc mode
5
Example: Infrastructure Mode
cnn.com
Internet
Access Point
Carey
(AP)
6
Example: Ad Hoc Mode
r Multi-hop “ad hoc” networking
Sean
Carey
7
Example: Ad Hoc Mode
r Multi-hop “ad hoc” networking
Sean
Carey
8
Example: Ad Hoc Mode
r Multi-hop “ad hoc” networking
Sean
Carey
9
Example: Ad Hoc Mode
r Multi-hop “ad hoc” networking
Sean
Carey
10
MAC Protocol Design
r Identify performance problems in wireless
Medium Access Control (MAC) protocols
r Examples: IEEE 802.11b WLANs
m
m
m
m
m
m
Unfairness problems [Xiao MSc 2004]
Effects of node mobility [Bai 2004]
“Bad Apple” phenomenon [Cao 2004]
TCP on multi-hop ad hoc networks [Gupta 2004]
Multi-channel MAC protocols [Kuang 2004]
Multi-rate multi-channel protocols [Wu 2005]
11
Network Traffic Measurement
r Collect and analyze packet-level traces
from a live network, using special equipment
r Process traces, statistical analysis
r Diagnose performance problems
(network, protocol, application)
12
Example: tcpdump Trace
0.000000 192.168.1.201 -> 192.168.1.200 60 TCP 4105 80 1315338075 : 1315338075 0 win: 5840 S
0.003362 192.168.1.200 -> 192.168.1.201 60 TCP 80 4105 1417888236 : 1417888236 1315338076 win: 5792 SA
0.009183 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338076 : 1315338076 1417888237 win: 5840 A
0.010854 192.168.1.201 -> 192.168.1.200 127 TCP 4105 80 1315338076 : 1315338151 1417888237 win: 5840 PA
0.014309 192.168.1.200 -> 192.168.1.201 52 TCP 80 4105 1417888237 : 1417888237 1315338151 win: 5792 A
0.049848 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417888237 : 1417889685 1315338151 win: 5792 A
0.056902 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417889685 : 1417891133 1315338151 win: 5792 A
0.057284 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417889685 win: 8688 A
0.060120 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417891133 win: 11584 A
0.068579 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417891133 : 1417892581 1315338151 win: 5792 PA
0.075673 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417892581 : 1417894029 1315338151 win: 5792 A
0.076055 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417892581 win: 14480 A
0.083233 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417894029 : 1417895477 1315338151 win: 5792 A
0.096728 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417896925 : 1417898373 1315338151 win: 5792 A
0.103439 192.168.1.200 -> 192.168.1.201 1500 TCP 80 4105 1417898373 : 1417899821 1315338151 win: 5792 A
0.103780 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417894029 win: 17376 A
0.106534 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417898373 win: 21720 A
0.133408 192.168.1.200 -> 192.168.1.201 776 TCP 80 4105 1417904165 : 1417904889 1315338151 win: 5792 FPA
0.139200 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417904165 win: 21720 A
0.140447 192.168.1.201 -> 192.168.1.200 52 TCP 4105 80 1315338151 : 1315338151 1417904890 win: 21720 FA
0.144254 192.168.1.200 -> 192.168.1.201 52 TCP 80 4105 1417904890 : 1417904890 1315338152 win: 5792 A
13
Example:
TELUS Mobility Project
r
Data Template and Example – XYZ Platform
Code
====
20
21
22
200
21x
22x
30
31
32
300
31x
32x
40
41
100
11x
12x
50
60
70
Definition
==========
FSCH Data Rate
FSCH Data Burst Start Time
FSCH Data Burst End Time
FSCH Active Set Report Time
FSCH Active Set Cell ID ('x' is a number)
FSCH Active Ste Sector ID ('x' is a number)
RSCH Data Rate
RSCH Data Burst Start Time
RSCH Data Burst End Time
RSCH Active Set Report Time
RSCH Active Set Cell ID ('x' is a number)
RSCH Active Ste Sector ID ('x' is a number)
FCH Data Start Time
FCH Data End Time
FCH Active Set Report Time
FCH Active Set Cell ID ('x' is a number)
FCH Active Ste Sector ID ('x' is a number)
IMSI
Frequency
SID
50
51
70
60
40
41
200
211
221
20
21
22
20
21
22
20
21
22
000006048421781
0x804ce0401aa89666
16422
384
2004041375333.680
2004041375443.020
2004041375337.940
32
3
16
2004041375338.200
2004041375339.200
4
2004041375357.860
2004041375357.880
16
2004041375371.720
2004041375372.700
14
Workload Characterization
r Try to understand the salient features of
network, protocol, application, and user
behaviour on the Internet
r Example: Web server workloads [Arlitt96]
m
m
m
m
m
Zipf-like document referencing behaviour
Lots of “one-time” referencing of documents
Heavy-tailed file size distributions
Self-similar network traffic profile
Session duration and call arrival process
15
Traffic Modeling
r Construct programs and statistical models
that capture the empirically-observed
network traffic behaviours
r Allows flexible, controlled, repeatable
generation of workloads for experiments
r Examples:
m
m
m
m
Web client workload model
MPEG compressed video model
Self-similar Ethernet LAN traffic model
WebTraff GUI: Web proxy workload generator
16
Example:
Web Workload Generation
17
Network Simulation
r Use computer simulation to study the
packet-level behaviour of the Internet, its
protocols, its applications, and its users
r Examples:
m
m
m
Improving Web performance over ADSL
Understanding the effects of user mobility on
Mobile IP routing and protocol performance
Studying the design, scalability, and
performance of Web server and Web proxy
caching architectures
18
Web Performance
r Explore techniques to improve the
performance and scalability of the Web
r Examples:
m
m
m
m
m
Clustered Web servers
Load balancing policies
Web prefetching strategies
Web proxy caching architectures
Improvements to HTTP and TCP protocols
19
Example:
Web Server Benchmarking
Client 1
Client 2
...
Web Server
Client 3
Client C
20
Summary
r Wireless Internet Performance Lab (UofC)
r Experimental Laboratory for Internet
Systems and Applications (UofS/UofC,CFI)
r iCORE Research Team:
m
Five full-time research staff (Web, perf. eval.,
simulation, wireless, traffic modeling, network
measurement) plus 8 graduate students
r Research Collaborations:
m UofC, UofA, UofS, TRLabs, CS/ECE
m HP, TELUS Mobility, SaskTel, Nortel…
r Industrially-relevant experimental
research on network protocol performance
21
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