Web Traffic Latency: Characteristics and Implications
1
Binzhang Liu and Edward A. Fox
Virginia Polytechnic Institute and State University, Virginia, USA
{bliu,fox}@vt.edu
Abstract: It is critical to understand WWW latency in order to
design better HTTP protocols. In this study we characterize Web response
time and examine the effects of proxy caching, network bandwidth, traffic
load, persistent connections for a page, and periodicity. Based on studies
with four workloads, we show that at least a quarter of the total elapsed
time is spent on establishing TCP connections with HTTP/1.0. The distributions
of connection time and elapsed time can be modeled using Pearson, Weibul,
or Log-logistic distributions. Response times display strong daily and
weekly patterns. We also characterize the effect of a user's network bandwidth
on response time. Average connection time from a client via a 33.6 K modem
is two times longer than that from a client via switched Ethernet. We estimate
the elapsed time savings from using persistent connections for a page to
vary from about a quarter to a half. This study finds that a proxy caching
server is sensitive to traffic loads. Contrary to the typical thought about
Web proxy caching, this study also finds that a single stand-alone squid
proxy cache does not always reduce response time for our workloads. Implications
of these results to future versions of the HTTP protocol and to Web application
design are discussed.
Keywords: World Wide Web, Latency, Characteristics, Implications
1 Introduction
In the past several years the World Wide Web has experienced tremendous
growth, and has become the dominant source of Internet traffic. Now millions
are using the Internet for WWW traffic. The Web has become an important
tool to access information. The Graphics, Visualization, & Usability
Center's (GVU) 8th WWW User Survey reported that "84% of the
users report that they considered access to the Web indispensable, nearly
the same percentage as those who feel email is indispensable, and 85% of
the users use it daily." [GVU 97]
HTTP/1.0 protocol is a simple request/response protocol, not designed
for heavy use. In order to accommodate continually increasing WWW uses,
HTTP needs to be effective and efficient. Based on studies of persistent
connections and pipelining, HTTP/1.1 was designed to improve the performance.
However, HTTP/1.1
[1] This is an extended version of a paper presented at the WebNet '98
conference in Orlando, Florida. The paper has received a "Top Full
Paper Award". Binzhang Liu was a graduate student in the Computer
Science Department at Virginia Polytechnic Institute and State University.
Now he works for Northern Telecom at Research Trangle Park, North Carolina,
USA.
deficiencies of complexity, poor extensibility, lack of generality and
poor scalability [Nielson 97]. To solve these deficiencies,
in July 1997, the World Wide Web Consortium (W3C) started the HTTP-NG project
to design the next generation of the HTTP protocol that fulfills these
requirements and resolves deficiencies of HTTP/1.1. The W3C group initiated
a wide range of Web characterization studies. The HTTP-NG activity statement
indicates "It is important to understand the actual system and how
it is being used before attempting to optimize it." [W3C
97] In the past, though many studies have been characterizing Web traffic,
little is known about the characteristics of Web latency. Web proxy caching
is widely used in the Web system, but little is known about the effectiveness
of proxy caching in improving Web latency. Research on these two issues
should enhance the understanding of the overall Web system. Hence, the
main objective of our study is to characterize Web response time. Specifically
this paper adopt an experimental approach to answer the following questions:
- What kind of distribution does response time follow?
- Does proxy caching improve response time?
- What is the effect of network bandwidth on response time?
- How does response time change with different levels of traffic?
- What kind of distribution is followed by the count of embedded images
in a page?
- How much elapsed time can be saved by persistent connections?
- Are there any periodic patterns for response time?
Related Work
Improving Web latency is a major research area. Many studies report
that speed continues to be the number one concern of Web users. Some people
even have called WWW the "World-Wide Wait". [GVU
95] Web users do not like to wait for a Web page that takes a long
time to retrieve. Web latency comes from many sources, involving the HTTP
protocol, Web server implementations, network bandwidth, characteristics
of Web documents, characteristics of Web clients and network topology.
The W3C Web Characterization Group emphasizes characterizing "the
kinds of tasks actually performed using HTTP, and the kinds of documents
that are retrieved ".[W3C 97] It also is important
to understand the nature of WWW latency in order to properly design, implement,
and improve the WWW system. Long latency is observed from some Web sites.
But the causes of long latency are not known yet. A study by Manley and
Seltzer concluded that the latencies can not be explained by server over-loading
and suggests that the bottleneck lies in the network [Manley
and Seltzer 97]. Another study found that bandwidth-related delay may
not account for much of the perceived latency [Panmanabhan
and Mogul 94]. One study by Touch, Heidemann and Obraczka reported
that most users see end-to end latencies of about 250 ms and concluded
that the persistent connections do not substantially affect Web latency
for the vast majority of users [Touch et al. 96].
Viles and French studied the availability and latency of the Web and suggested
shorter client-side time-out intervals than those used for TCP connection
establishment [ Viles and French 95]. The studies
by the NRG group at Virginia Tech have shown that 30% to 50% hit rates
can be achieved by proxy caching [Williams et al. 1997].
Prefetching attempts to predict future accesses, and pre-loads documents
into the cache; this may significantly improve network performance [Crovella
and Barford 1997]. Another
study found that Web resources change frequently and so suggested limits
on the utility of simple caches [Douglis et al. 98].
To our knowledge there was no study to characterize Web response time.
3 Workloads Used in the Study
Four proxy log files are used in the study. The America Online workload
(AOL) is about 40 minutes worth of proxy log file from America Online's
proxy server. The Boston University workload (Boston) is a proxy log file
in the Computer Science Department at Boston University. The VT Campus
workload (VT) is a proxy log file from the Virginia Tech campus-wide proxy
server. VT Library (VTLIB) is a proxy log file from the Newman Library
proxy server at Virginia Tech. Table 1 describes these workloads. For additional
details, see [Abdulla 98].
Table 1: Workloads used in the study
Workloads
|
Periods
|
Total Accesses
|
America Online
|
Dec. 1, 1997
|
825,602
|
Boston University
|
Jan. 27 to Feb. 8, 1995
|
522,928
|
VT Campus
|
Sep. 28 to Oct. 5, 1997
|
696,975
|
VT Library
|
Sep. 28 to Oct. 5, 1997
|
1,014,875
|
4 Experiments
In our experiments, we use Webjamma [Johnson 97]
to re-play log files. Webjamma replays a workload by reading a log file
of URLs, sending HTTP queries in those logs, and timing the transfers.
Since Webjamma just discards the transfered data, the only delay is from
the transfer. Webjamma submits a configurable number of HTTP requests in
parallel. In the proxy caching experiments, we used a modified version
of squid 1.1.6 [Johnson 97]. Connection time
is defined as the time between when a browser tries to set up a TCP connection
to a Web server or proxy server and the first byte is received by the browser.
Transfer time is the time between when a browser receives the first
byte from a Web server or proxy server and the browser receives the last
byte. Elapsed time is equal to connection time plus transfer time.
By varying five variables - proxy option, connection type, network bandwidth,
number of Webjamma processes, and time ten experiments were designed to
allow explantation of the problems listed before. The first factor, proxy
option, is either none, where the HTTP queries are sent directly to the
original server, or one, where the HTTP queries are sent to a proxy cache,
which then sends them directly to the server. The second factor, connection
type, represents the
type of HTTP connections (persistent or non-persistent) used. The third
factor, bandwidth, reflects the type of network connection between the
the browser and Internet. The fourth factor, number of Webjamma processes,
is used to simulate different load levels on the proxy server. The last
factor is time. Re-playing log files at different hours of a day and different
days of a week allows us to examine the periodicity of Web response time.
Table 2 lists detailed information for each experiment including workload
used, sample size, network connection, number of Webjamma processes and
proxy option.
Table 2: Detailed description of the ten experiments
Experiment |
Workload
|
Sample Size
|
Network Connection
|
No. of Webjamma Processes
|
Proxy Option
|
1
|
AOL, Boston, VT, VTLIB
|
Full Log
|
Ethernet
|
20
|
No
|
2
|
VT
|
10K
|
Modem
|
1
|
No
|
3
|
VT
|
Full Log
|
Ethernet
|
1, 20
|
Yes
|
4
|
VT
|
10K
|
Modem
|
1
|
Yes
|
5
|
VT
|
10K
|
Ethernet
|
1, 10, 20, 30, 50, 70, 90
|
Yes
|
6
|
AOL, VT
|
1K
|
Ethernet
|
1
|
No
|
7
|
AOL, VT
|
1K
|
Modem
|
1
|
No
|
8
|
AOL, VT
|
1K
|
Ethernet
|
1
|
Yes
|
9
|
AOL, VT
|
1K
|
Ethernet
|
1
|
Yes
|
10
|
VT
|
10K
|
Ethernet
|
20
|
No
|
5 WWW Response Time Without a Proxy Cache
5.1 Response Time Via Switched Ethernet Without a Proxy Cache
From the data resulting from experiment one, average connection and
elapsed time were calculated using the PERL scripts developed during this
study. Table 3 gives average connection time, average elapsed time and
ratio of connection time to elapsed time for the four workloads without
a proxy. Table 3 shows that average connection time ranged from a low of
0.27 to a high of 0.54 seconds, and average elapsed time ranged from a
low of 0.57 to a high of 2.0 seconds. The ratio of average connection time
to average elapsed time ranged from a low of 0.27 to a high of 0.69. In
all workloads, this ratio is higher than 0.25. It indicates that at least
a quarter of the total elapsed time was spent in setting up the connection.
To compare the response time of local and remote accesses, the VT Campus
and VT Library workloads were split into local accesses (.vt.edu domain)
and remote accesses (domain other than .vt.edu). Table
4 lists the average connection time
Table 3: Average connection time, elapsed time and ratio
of connection time to elapsed time
Workload
|
Connection Time
|
Elapsed Time
|
Ratio
|
America Online
|
0.54
|
1.98
|
0.27
|
Boston University
|
0.39
|
0.57
|
0.69
|
VT Campus
|
0.27
|
0.73
|
0.36
|
VT Library
|
0.32
|
0.88
|
0.36
|
Note: All time is in seconds.
and average elapsed time for local and remote accesses. Both average
connection time and average elapsed time for local accesses are shorter
than for remote accesses.
Table 4: Response time for local and
remote accesses
Workload
|
Average Connection Time
|
Average Elapsed Time
|
Local
|
Remote
|
Local
|
Remote
|
VT Campus
|
0.20
|
0.31
|
0.47
|
0.76
|
VT Library
|
0.22
|
0.34
|
0.34
|
0.98
|
Note: All time is in seconds
Figure 1 shows that over 90% of the time the connection time is less
than one second. Figure 2 shows that about 80% of the
time the elapsed time is less than one second. The cumulative distributions
of connection time and elapsed time follow Pearson distributions except
the cumulative distribution of connection time of the VT campus workload
follows a Weibul distribution. Tables 5 and 6 list
the parameters of distributions for connection time and elapsed time. See
[ Law and Vincent 94] for a detailed description
of various distribution functions.
Table 5: Parameter estimates of distribution of connection
time

Table 7 lists the connection time under various cumulative frequencies.
For all workloads, 99% of the time, the connection time is less than 10
seconds. This

Figure 1: Cumulative distribution of connection time

Figure 2: Cumulative distribution
of elapsed time
result suggests that a Web client default timeout value should not be
longer than 10 seconds.
Table 6: Parameter estimates of distribution
of elapsed time

Table 7: Connection time for various cumulative frequencies
Workloads |
90% |
99% |
99.9% |
America Online |
0.90 |
9.69 |
22.56 |
Boston University |
0.62 |
5.14 |
14.12 |
VT Campus |
0.40 |
3.74 |
13.98 |
VT Library |
0.46 |
4.53 |
22.58 |
Note: All time is in seconds
5.2 Response Time Via a 33.6 K Modem Without a Proxy Cache
To examine the effects of network connection on response time, in experiment
two we chose a subset of the VT Campus proxy workload and re-played it
using Webjamma via a 33.6 K modem connection. The average connection time
from a client via 33.6 K modem network connection is 0.59 seconds (2.2
times longer than that from a client using switched Ethernet). Average
elapsed time from a client via a 33.6 K modem connection is 2.33 seconds
(3.19 times longer than that via switched Ethernet). Table 8 lists the
connection and elapsed time under
Table 8: Connection time of modem users under various
cumulative frequencies
Workloads |
90% |
99% |
99.9% |
VT Campus |
0.79 |
4.46 |
13.12 |
Note: All time is in seconds
various cumulative frequencies. Table 8 shows that 99% of the time,
connection time via a 33.6 K modem is less than 4.5 seconds.
6 Web Response Time with a Proxy Cache
In experiments three and four, the VT campus workload was re-played
to a proxy server in the Computer Science Department. The results are summarized
Table 9: Response time of proxy caching using VT campus
workload
Number of Processes
|
Network Connection
|
Average Connection Time
|
Average Elapsed Time
|
Ratio1
|
Ratio2
|
1
|
Ethernet
|
0.47
|
0.86
|
1.76
|
1.18
|
20
|
Ethernet
|
0.81
|
1.43
|
3.03
|
1.96
|
1
|
Modem
|
0.87
|
2.50
|
1.48
|
1.07
|
Note: All time is in seconds
in Table 9, where ratio1 is the ratio of the connection time with a
proxy to the connection time without a proxy. Ratio2 is the ratio of the
elapsed time with a proxy to the elapsed time without a proxy.
Table 9 shows that for Ethernet users, if proxy traffic load is low
(e.g., in the one process case) average connection time is about 1.76 times
longer and the elapsed time is about 1.18 times longer than that without
a proxy. If proxy traffic load is heavy (e.g., in the 20 process case),
average connection time is about three times longer and the average elapsed
time is about two times longer than that without a proxy. For a 33.6 K
modem user, with a proxy the average connection time is 1.48 times longer,
and average elapsed time is almost the same as the average elapsed time
without a proxy. These results indicate that proxy caching increases both
average connection time and elapsed time. It also indicates that increased
traffic loads degrade the performance of a proxy caching server.
Table 10: Connection time of VT workload with a proxy
under various cumulative frequencies
Option |
90% |
99% |
99.9% |
proxy |
1.4 |
12.2 |
25.21 |
Note: All time is in seconds
Figure 3 shows that over 80% of the time the connection time is less
than one second and about 80% of the time the elapsed time is less than
1.3 seconds. Table 11 lists the connection time of
VT campus proxy traffic for local and remote accesses under various cumulative
frequencies.

Figure 3: Cumulative distribution of connection time
and elapsed time
Table 11: Connection time of VT campus
proxy workload for local and remote accesses under various cumulative frequencies
Option |
90% |
99% |
99.9% |
local |
1.2 |
2.2 |
9.9 |
remote |
1.4 |
12.3 |
25.3 |
Note: All time is in seconds
7 Response Time and Proxy Traffic Loads
In experiment five, the number of parallel Webjamma processes ranges
from 1 to 90, whence the corresponding completed requests per second range
from a low of 0.65 to a high of 20.83. Earlier, Slothouber [Slothouber
96] studied Web server performance using a queuing model and found
that before a server reached its theoretical upper bound load, the response
time was almost constant (a mere fraction of a second). When the server
approached full utilization, response time grew asymptotically toward infinity.
Contrary to his findings, our results show that proxy server performance
is generally sensitive to traffic load. Even at very low request arrival
rates, when the number of processes increases from 1 to 10 (equivalent
to a range of 56,471 accesses/day to 436,363 accesses/day), the average
connection time increases by 37%, and the average elapsed time increases
by 38%. In Figure 4, Ratio represents the ratio of the average connection
time to the average elapsed time. Figure 4 shows that response time increases
with an increase in the request arrival rate, but when that rate goes beyond
16 per second (1.38 million per day), the response curve becomes steep
and the ratio of average connection time to average elapsed time is increasing.

Figure 4: Response curve of response time to proxy traffic
load
8 WWW Response Time With Persistent Connections
To examine how persistent connections affect response time, experiments
six, seven, eight and nine were conducted. In this study, we simulate persistent
connections from clients to Web servers for a page, which we define as
having multiple objects including an HTML file and in-line image files.
Connection time of a page for non-persistent and persistent connections
is the connection time of the first object in a page. Elapsed time for
a non-persistent connection is calculated by summing the elapsed times
of all objects in a page. Elapsed time of a page for a persistent connection
is the connection time of the HTML file plus the sum of the transfer times
of each object in a page.
8.1 Distribution of Number of Embedded Images
The average number of unique in-line image files in a page for the AOL
workload is 3.33; for the VT workload it is 2.56. About one third of our
HTML pages do not have an embedded image. 65% of the pages contain at least
one unique embedded image in the AOL workload. In the VT workload, about
46% of the pages do not have an embedded image and 54% of the pages contain
at least one unique embedded image. The maximum number of unique embedded
images in the AOL workload is 39; for the VT workload it is 36. Distributions
of the number of unique embedded images in a page follow a Random Walk,
which is a type of heavy tailed distribution. Table 12 lists the parameter
estimates of the distributions. Figure 5 shows that for 80% of the pages
the number of unique embedded images is less than 5, and for over 10% of
the pages the number of unique embedded images is over 10.

Figure 5: Distribution of number of unique embedded images
in a page
Table 12: Parameter estimates of distribution of number
of embedded images in a page

8.2 Percentage of Elapsed Time Saving From Persistent Connection
Table 13: Percentage of elapsed time saving from persistent
connection

According to Table13, the percentage of elapsed time saving from persistent
connections ranges from a high of 49.97% (10baseT switched Ethernet with
a proxy for AOL workload) to a low of 22.78% (10baseT switched Ethernet
without a proxy for AOL workload). These results show that elapsed time
savings from using persistent connections are significant.
9 Periodicity of WWW Response Time
A study by Abdulla et al. found that Web traffic displayed strong periodicity
[Abdula et al. 97]. In order to examine whether
periodicity exists for Web response time, experiment ten was conducted.
Two time series - average connection time and average elapsed time were
calculated from the results of experiment ten. These two time series are
used in the analysis to identify periodicity of Web response time.
9.1 Plot of the Response Time Series
To visualize the periodicity of Web response time, a plot of average
connection time (ct) and average elapsed time (et) is drawn. Figure 6 shows
the time plot.

Figure 6: Time plot of response time series
In Figure 6, ct and et designate average connection time and average
elapsed time. Figure 6 is the time plot of connection time and elapsed
time over a 333 hour period. By counting the main peaks, it is noticed
that there are about 14 peaks in the plot. This suggests that a period
is approximately 24 hours (one day).
9.2 Spectral Analysis
Spectral analysis is often used in looking for periodicity in data.
In this study, the SAS spectra procedure is used to produce estimates of
the spectral densities
of time series data, and then these estimates are used to obtain periodograms.
The Fisher's Kappa test is also specified to test white noise. The Fisher's
Kappa statistics for both series are larger than the 5% critical value
7.2, so the null hypothesis that the time series data are white noise is
rejected. Figure 7 shows the plot of periodograms
of both time series (connection time and elapsed time).

Figure 7: Plot of periodogram by period
From Figure 7 the periodicity in the time series is very clear. The
peaks in the figure correspond to the major periods in the data. One main
peak at around 24 hours corresponds to the daily cycle. Another small peak
at around 168 hours corresponds to the weekly cycle.
9.3 Correlation of Response Time and Web Traffic
To find out whether this periodic pattern is related to the Web traffic,
we extracted hourly accesses to the VT campus proxy from its log files
for the period of Jan. 15, 1998 to Jan. 29, 1998. The regression analysis
for average connection time and average hourly accesses to the VT campus
proxy and the Computer Science courseware Web server ei.cs.vt.edu are shown
in Tables 14 and 15.
Coefficients of the access count variable in Tables 14 and 15 are significant.
The Web response time is highly correlated with the VT campus proxy traffic
and the Web traffic to the ei.cs.vt.edu server during the same period (Jan.
15 to Jan. 29, 1998). It is interesting that the Web response time is more
highly correlated with the Web traffic to the ei.cs.vt.edu than the VT
campus proxy traffic. Since the coefficient of the access count variable
is positive, an increase in the access count will lead to longer response
time. This result indicates that Web traffic is at least partially responsible
for the long Web latency.
Table 14: Regression analysis of the connection time
and hourly accesses to the VT campus proxy
Variables |
Coefficient |
T ratio |
Constant |
0.31 |
6.64 |
Accesses |
8.6E-05 |
3.89 |
Note:R2=0.41, F=15.11
Table 15: Regression analysis of the connection time
and hourly accesses to the ei.cs.vt.edu Web server
Variables |
Coefficient |
T ratio |
Constant |
0.16 |
4.06 |
Accesses |
0.0003 |
8.24 |
Note:R2=0.76, F=67.84
10 Conclusion
In this study using four workloads we conducted ten experiments. We
characterized Web response time and examined effects on response time of
proxy caching, network bandwidth, traffic loads, persistent connections
for a page, and periodicity. The following conclusions are drawn from the
results above.
- Connection time is a major component of total elapsed time for HTTP/1.0.
In all four workloads, for a non-persistent connection, the ratio of connection
time to elapsed time is higher than 0.25. At least a quarter of the total
elapsed time is spent in establishing a network connection.
- Distributions of connection time and elapsed time follow Pearson, Weibul
or Log-logistic distributions. For both low speed modem users, and users
with switched Ethernet connection, 99% of the time, connection time will
be less than 10 seconds. This result suggests that Web client timeout values
should not be higher than 10 seconds.
- Contrary to popular thoughts, results from several experiments indicate
that at least in our cases proxy caching does not necessarily result in
shorter Web response time. In our cases, it almost always increases response
time. In order to achieve better performance in terms of response time,
proxy systems should be better designed, and relevant HTTP protocol changes
on proxy caching should be made. We found in the proxy log file over 10%
accesses are ``not modified" (304 status code). Proxies can be designed
to allow for a distribution model to validate cache contents and hence
``conditional Get" will not be necessary.
- The response curve of a proxy server (squid 1.1.6) to traffic load
is not flat even at a very low request rate. Response time increases with
an increase in request arrival rate. This indicates that a proxy caching
server is sensitive to traffic load. Contrary to our empirical results,
using queuing theory Slothouber found that as the load on the Web server
increases the time required to serve a file increases very gradually (almost
imperceptibly) [Slothouber 96].
This may indicate that Web server performance can't be correctly analyzed
using a simple queuing model.
- The results show that using persistent connections can achieve a significant
performance improvement in terms of elapsed time. A 23% to 50% elapsed
time saving can be achieved by using persistent connections. The amount
of elapsed time saving from persistent connections is a function of the
number of embedded images in a page. For the VT workload, under persistent
connections proxy caching can lower elapsed time.
- Speed of network connection has a significant effect on the response
time. Connection time from a client with 33.6 K modem is 2.2 times longer
than that from a client with a switched Ethernet connection. For modem
users, connection time still constitutes a large part of total elapsed
time. In our case, connection time is still 25% of total elapsed time for
modem users without a proxy. It is not necessarily the case that the percentage
of time saved from using persistent connections for a page via a modem
is less than that via high speed Ethernet. Contrary to Touche's result
[Touch et al. 96], this result suggests that even
for modem users, migrating client browsers to HTTP/1.1 compatible versions
can achieve a significant response time improvement.
- The distribution of the number of unique embedded images in a page
is not normal; rather it follows a Random Walk distribution. The average
number of unique in-line image files in a page for the AOL workload is
3.33. The average number of unique in-line image files in a page for the
VT workload is 2.56. Over one third of pages do not contain an embedded
image. In the HTTP/1.1 performance study by a W3C group [W3C
1997], they used a page with 42 in-line GIF images. This test page
is not representative, so their HTTP/1.1 performance results may be biased.
- There exist daily and weekly periodic patterns for response time. The
periodicity of Web response time indicates that the present Web system
is overloaded. This result also provides the rationale for timing of pre-fetching,
Web crawler and Web indexing activities. These activities should be scheduled
to run at low points in the cycle.
- Web response time has a very high variability. Average connection time
at the peak point is about 3.28 times longer than at the bottom point in
the daily cycle. We found that Web response time is highly correlated with
Web traffic. This suggests that Web traffic is at least partially responsible
for high latency.
Acknowledgments
Members of the VT Network Research Group (NRG) provided helpful comments
on the manuscript. NSF grants CDA-931261 and NCR-9627922 partially supported
this work. IBM donated equipments used to collect and process the traffic
log files.
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