Multi-Layer Analysis of Web Browsing Performance for Wireless PDAs Adesola Omotayo & Carey...
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Multi-Layer Analysis of Web Browsing Performance for
Wireless PDAs
Multi-Layer Analysis of Web Browsing Performance for
Wireless PDAs
Adesola Omotayo & Carey WilliamsonAdesola Omotayo & Carey Williamson
April 18, 2023April 18, 2023
2
Presentation OutlinePresentation Outline
Introduction & MotivationRelated WorkData Gathering & ValidationHTTP-level AnalysisTCP-level AnalysisMAC-level & Error AnalysisSummaryFuture Work
Introduction & MotivationRelated WorkData Gathering & ValidationHTTP-level AnalysisTCP-level AnalysisMAC-level & Error AnalysisSummaryFuture Work
3
Introduction & MotivationIntroduction & Motivation
Widespread availability of WiFi hot spots
Limited understanding of multi-layer protocol interactions over IEEE 802.11b WLAN
Crucial to understand the performance of the wireless Web
Widespread availability of WiFi hot spots
Limited understanding of multi-layer protocol interactions over IEEE 802.11b WLAN
Crucial to understand the performance of the wireless Web
4
Related WorkRelated Work
Workload of clients at wireline networksClient-based
“Changes in Web Client Access Patterns”,P. Barford, A. Bestavros, A. Bradley, and M. Crovella, 1999
Server-based“Internet Web Servers: Workload Characterization and Performance Implications”,M. Arlitt and C. Williamson, October 1997
Proxy-based“On the Scale and Performance of Cooperative Web Proxy Caching”,A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy, December 1999
Workload of wireless clientsLocal-area
“Analysis of a Local-Area Wireless Network”, D. Tang and M. Baker, August 2000
Campus-area“Analysis of a Campus-Wide Wireless Network”, D. Kotz and K. Essien, September 2002
Metropolitan-area“Analysis of a Metropolitan-Area Wireless Network”, D. Tang and M. Baker, August 1999
Workload of clients at wireline networksClient-based
“Changes in Web Client Access Patterns”,P. Barford, A. Bestavros, A. Bradley, and M. Crovella, 1999
Server-based“Internet Web Servers: Workload Characterization and Performance Implications”,M. Arlitt and C. Williamson, October 1997
Proxy-based“On the Scale and Performance of Cooperative Web Proxy Caching”,A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy, December 1999
Workload of wireless clientsLocal-area
“Analysis of a Local-Area Wireless Network”, D. Tang and M. Baker, August 2000
Campus-area“Analysis of a Campus-Wide Wireless Network”, D. Kotz and K. Essien, September 2002
Metropolitan-area“Analysis of a Metropolitan-Area Wireless Network”, D. Tang and M. Baker, August 1999
5
Data Gathering & ValidationData Gathering & Validation
Selected websitesnews, yellow pages, driving directions, stock quotes, educational resources, and downloadable PDA software
Over a period of 35 minutes
398 TCP connections1.8% with expected FIN handshake96.5% used the RST packet 1.7% unsuccessful connections
Selected websitesnews, yellow pages, driving directions, stock quotes, educational resources, and downloadable PDA software
Over a period of 35 minutes
398 TCP connections1.8% with expected FIN handshake96.5% used the RST packet 1.7% unsuccessful connections
Wireless Client
Internet
Wired Network
Access Point
Wireless Sniffer
A very simple workloadA very simple workload
AP: Netgear WAB 102
PDA: Compaq iPAQ 3600 Pocket PC, Windows CE, IE, MTU size of 1500 bytes
Wireless Sniffer: Sniffer Pro 4.60.01, microsecond resolution timestamps
6
HTTP-level AnalysisHTTP-level AnalysisServer Response TimeServer Response Time
distinct plateaus
consistent server response time
response times < 200 ms
distinct plateaus
consistent server response time
response times < 200 ms
Network RTT dominates the response latency
Cache per-destination state information
Network RTT dominates the response latency
Cache per-destination state information
Server Response Time versus Connection ID
yahoo
fore
cast
erfo
reca
ster
hmdns
quickdrive
atlantic
tuco
ws
cpsc
i-m
ecc
a
tucows
airsurfer
CNN
cpsc
tucows
akam
aifa
ntas
yspo
rts
fant
asys
port
s
cnet
akam
ai
cpsc
weather3
cpsc UofC
chutch
akam
aicn
et
cnet
0
0.05
0.1
0.15
0.2
0.25
0 50 100 150 200 250 300 350
Connection ID
Se
rve
r R
es
po
ns
e T
ime
in
se
co
nd
s
Server Response Time versus Connection ID
yahoo
fore
cast
erfo
reca
ster
hmdns
quickdrive
atlantic
tuco
ws
cpsc
i-m
ecc
a
tucows
airsurfer
CNN
cpsc
tucows
akam
aifa
ntas
yspo
rts
fant
asys
port
s
cnet
akam
ai
cpsc
weather3
cpsc UofC
chutch
akam
aicn
et
cnet
0
0.05
0.1
0.15
0.2
0.25
0 50 100 150 200 250 300 350
Connection ID
Se
rve
r R
es
po
ns
e T
ime
in
se
co
nd
s
7
HTTP-level AnalysisHTTP-level Analysis
Web Object SizesWeb Object Sizesobject sizes:
90% < 10 KB2.5% > 40 KB
file types:most prevalent: GIF, JPG & HTMLLeast prevalent: PNG
largest objects transferred:executables
object sizes:90% < 10 KB2.5% > 40 KB
file types:most prevalent: GIF, JPG & HTMLLeast prevalent: PNG
largest objects transferred:executables
Cache contents from wireless portals on Proxy Servers
Increase support for PNG file type across browsers
Compress executable files to be more compact
Cache contents from wireless portals on Proxy Servers
Increase support for PNG file type across browsers
Compress executable files to be more compact
Distribution of HTTP Transfer sizes
0
5
10
15
20
25
30
0 5000 10000 15000 20000 25000 30000 35000 40000
HTTP Transfer Size in Bytes
Fre
qu
ency
in
Per
cen
t
Distribution of HTTP Transfer sizes
0
5
10
15
20
25
30
0 5000 10000 15000 20000 25000 30000 35000 40000
HTTP Transfer Size in Bytes
Fre
qu
ency
in
Per
cen
t
8
HTTP-level AnalysisHTTP-level Analysis
HTTP Transfer TimeHTTP Transfer TimeScatter Plot of HTTP Response Time
0.001
0.01
0.1
1
10
100
1000
1 10 100 1000 10000 100000 1000000
HTTP Response Size in Bytes
Tra
nsfe
r T
ime i
n S
eco
nd
s
Scatter Plot of HTTP Response Time
0.001
0.01
0.1
1
10
100
1000
1 10 100 1000 10000 100000 1000000
HTTP Response Size in Bytes
Tra
nsfe
r T
ime i
n S
eco
nd
s HTTP transfers96% < 1 second2.5% > 2 seconds
larger objects take longer to download
few small objects have excessively long transfer times
HTTP transfers96% < 1 second2.5% > 2 seconds
larger objects take longer to download
few small objects have excessively long transfer times
HTTP transfer times are generally low
Most responses fit in a single TCP packet
HTTP transfer times are generally low
Most responses fit in a single TCP packet
9
TCP-level AnalysisTCP-level Analysis
TCP Connection TypeTCP Connection TypeNumber of HTTP Requests Per Connection
akam
ai
216.2
39.3
9.9
9216.2
39.3
9.9
9
akam
ai
adminUofCgoogle
akamaiquickdrive
CNN
0
10
20
30
40
50
60
70
80
0 51 102 152 204 254 304 355
Connection ID
Nu
mb
er
of
HT
TP
Req
uests
Number of HTTP Requests Per Connection
akam
ai
216.2
39.3
9.9
9216.2
39.3
9.9
9
akam
ai
adminUofCgoogle
akamaiquickdrive
CNN
0
10
20
30
40
50
60
70
80
0 51 102 152 204 254 304 355
Connection ID
Nu
mb
er
of
HT
TP
Req
uests
13% were persistent
87% were non-persistent
4% of TCP connections sent > 10 HTTP requests
65% of HTTP transfers occurred on persistent connections
As much as 73 HTTP requests were seen per connection
13% were persistent
87% were non-persistent
4% of TCP connections sent > 10 HTTP requests
65% of HTTP transfers occurred on persistent connections
As much as 73 HTTP requests were seen per connection
Use persistent connections for all web sitesUse persistent connections for all web sites
10
TCP-level AnalysisTCP-level Analysis
TCP Connection DurationTCP Connection Duration75% sent < 20 packets
6% sent > 100 packets
80% sent < 10 KB
8% sent > 50 KB
75% lasted < 1 second
10% lasted > 30 seconds
4 connections lasted > 300 sec.
75% sent < 20 packets
6% sent > 100 packets
80% sent < 10 KB
8% sent > 50 KB
75% lasted < 1 second
10% lasted > 30 seconds
4 connections lasted > 300 sec.
Most TCP connections are non-persistent
Most web object transfers are small
Tightly set the persistent connection timeout
Most TCP connections are non-persistent
Most web object transfers are small
Tightly set the persistent connection timeout
Distribution of TCP Connection Duration
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30
Connection Duration in Seconds
Fre
qu
en
cy in
Perc
en
t
Distribution of TCP Connection Duration
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30
Connection Duration in Seconds
Fre
qu
en
cy in
Perc
en
t
11
TCP-level AnalysisTCP-level Analysis
TCP Connection ThroughputTCP Connection Throughput
Connection Throughput in bits per second (bps)
14
12
10
8
6
4
2
00 0 200000 400000 600000 800000 1e+06 1.2e+06
Distribution of TCP Connection Throughput
Fre
qu
ency
in
Per
cen
t
95% < 400 Kbps95% < 400 Kbps
Non-persistent TCP connections
Small HTTP transfer size
Non-negligible RTTs
TCP slow start effects
Non-persistent TCP connections
Small HTTP transfer size
Non-negligible RTTs
TCP slow start effects
12
MAC-level & Error AnalysisMAC-level & Error Analysis
MAC-level RetransmissionsMAC-level Retransmissions
3% of the packets
40% of the connections
most retry attempts for a packet: 6
3% of the packets
40% of the connections
most retry attempts for a packet: 6
CRC ErrorsCRC Errors
0.04% of the packets0.04% of the packets
TCP-level RetransmissionsTCP-level Retransmissions
0.2% of the packets
12 TCP connections
2 connection have > 3 packet loss
0.2% of the packets
12 TCP connections
2 connection have > 3 packet loss
HTTP-level ErrorsHTTP-level Errors
Unsuccessful: 1%
Successful: 96.74%
Aborted: 2.26%
Unsuccessful: 1%
Successful: 96.74%
Aborted: 2.26%
Wireless channel quality does not have a major impact on wireless Web browsing performance
Wireless channel quality does not have a major impact on wireless Web browsing performance
13
Summary (1 of 2)Summary (1 of 2)
FactsFacts ImplicationsImplications
Network RTT dominates the response latency
Caching per-destination state information (e.g., RTT, cwnd) might be effective
Web objects are typically smallWeb proxy caching of content from wireless portals could reduce network latency
Largest web objects transferred were executables
Software providers should compress executable files into more compact file formats
Even though free, the least prevalent graphics file type on the web is PNG
Increase support for PNG file type across web browsers
14
FactsFacts ImplicationsImplications87% were non-persistent and 65% of HTTP transfers occurred on persistent connections
Wireless Web browsing would be faster if persistent connections were used for all Web sites
Some TCP connections lasted longer than 300 seconds
Persistent connection timeout should be tightly set
52% of the TCP packets were transmitted by the client PDA
Some form of ACK consolidation in Windows CE would economize on wireless network usage and battery power for wireless device
MAC: 3% of the packetsCRC: 0.04% of the packetsTCP: 0.2% of the packetsHTTP: 1% of the connections
Wireless channel quality does not have a major impact on wireless Web browsing performance
Summary (2 of 2)Summary (2 of 2)
15
Future WorkFuture Work
Expand the work to a large scale traffic measurement
Study the effect of interference and range overlapping among closely located APs
Expand the work to a large scale traffic measurement
Study the effect of interference and range overlapping among closely located APs
16
ReferencesReferences
M. Arlitt and C. Williamson, “Internet Web Servers: Workload Characterization and Performance Implications”, IEEE/ACM Transactions on Networking, Vol. 5, No. 5, pp. 631-645, October 1997.
P. Barford, A. Bestavros, A. Bradley, and M. Crovella, “Changes in Web Client Access Patterns”, World Wide Web Journal, 1999.
D. Kotz and K. Essien, “Analysis of a Campus-Wide Wireless Network”, Proceedings of ACM MOBICOM, Atlanta, GA, pp. 107-118, September 2002.
D. Tang and M. Baker, “Analysis of a Metropolitan-Area Wireless Network”, Proceedings of ACM MOBICOM, Seattle, WA, pp. 13-23, August 1999.
D. Tang and M. Baker, “Analysis of a Local-Area Wireless Network”, Proceedings of ACM MOBICOM, Boston, MA, pp. 1-10, August 2000.
A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy, “On the Scale and Performance of Cooperative Web Proxy Caching”, Proceedings of ACM SOSP, December 1999.
M. Arlitt and C. Williamson, “Internet Web Servers: Workload Characterization and Performance Implications”, IEEE/ACM Transactions on Networking, Vol. 5, No. 5, pp. 631-645, October 1997.
P. Barford, A. Bestavros, A. Bradley, and M. Crovella, “Changes in Web Client Access Patterns”, World Wide Web Journal, 1999.
D. Kotz and K. Essien, “Analysis of a Campus-Wide Wireless Network”, Proceedings of ACM MOBICOM, Atlanta, GA, pp. 107-118, September 2002.
D. Tang and M. Baker, “Analysis of a Metropolitan-Area Wireless Network”, Proceedings of ACM MOBICOM, Seattle, WA, pp. 13-23, August 1999.
D. Tang and M. Baker, “Analysis of a Local-Area Wireless Network”, Proceedings of ACM MOBICOM, Boston, MA, pp. 1-10, August 2000.
A. Wolman, G. Voelker, N. Sharma, N. Cardwell, A. Karlin, and H. Levy, “On the Scale and Performance of Cooperative Web Proxy Caching”, Proceedings of ACM SOSP, December 1999.