Characterization of E-Commerce Traffic.
The World Wide Web has achieved immense popularity in the business world. It is thus essential to characterize the traffic behavior at these sites, a study that will facilitate the design and development of high-performance, reliable e-commerce servers. This paper makes an effort in this direction. Aggregated traffic arriving at a Business-to-Business (B2B) and a Business-to-Consumer (B2C) e-commerce site was collected and analyzed. High degree of self-similarity was found in the traffic (higher than that observed in general Web-environment). Heavy-tailed behavior of transfer times was established at both the sites. Traditionally this behavior has been attributed to the distribution of transfer sizes, which was not the case in B2C space. This implies that the heavy-tailed transfer times are actually caused by the behavior of back-end service time. In B2B space, transfer-sizes were found to be heavy-tailed. A detailed study of the traffic and load at the back-end servers was also conducted and the inferences are included in this paper.
Introduction
The explosive popularity of Internet has propelled its usage
in several commercial avenues. E-commerce, the usage of
Internet for buying and selling products, has found a major
presence in today's economy. E-commerce sites provide
up-to-date information and services about products to users
and other businesses. Services ranging from personalized
shopping to automated interaction between corporations are
provided by these web-sites. It has been reported that e-commerce
sites generated $132 Billion in 2000, more than
double of the $58 Billion reported in 1999 [1]. Even though
the power of the servers hosting e-commerce sites has been
increasing, e-commerce sites have been unable to improve
their level of service provided to the users. It has been reported
that around $420 Million has been lost in revenues
due to slow processing of the transactions in 1999. Thus it
is desirable and necessary to focus on the performance of
the servers used in these environments.
There are two main classes of e-commerce sites,
Business-to-Business (B2B) and Business-to-Consumer
(B2C), providing services to corporations and individual
users respectively. Web sites like Delphi, which provide services
to corporations like General Motors come under B2B
sites, whereas sites like Amazon.com providing services to
general users come under B2C sites.
This work was supported in part by the National Science Foundation.
In this paper, we have analyzed the characteristics of e-commerce
traffic. Traffic from a B2C and a B2B site is being
used for the study. The workload is initially inspected
for understanding the diurnal nature of the traffic. Different
load periods were identified for both the B2C and B2B en-
vironments. These have been found to be complimentary in
nature, which may be intuitive. A set of parameters were
chosen for each site for each component which would impact
the performance of the system to the maximum extent.
Statistical tests are then used to prove the self-similar nature
of the traffic at different scales. Two different tests are
used for validating the results for each of the parameters.
It has been observed that the arrival traffic is highly bursty
in nature, much more than the burstiness seen in normal
web-traffic [5]. The response-time distribution is found to
be heavy-tailed. This has been previously attributed to the
heavy-tailed nature of request and response file-sizes. But
the behavior of transfer sizes is not heavy-tailed, unlike the
general web-environment. The traffic arriving at the back-end
servers is characterized to obtain similar statistics about
the impact of burstiness on the system. Also preliminary
tests have shown that the back-end utilization is more bursty
than the front-end server utilization, the reasons for which
are explained later. A correlation is drawn between the behavior
of the front-end and the back-end servers under different
load conditions. Performance implications from the
results of the above experiments will give valuable information
for improving e-commerce server performance.
The workload characterization studied in this paper is
based on one representative system from each of the environments
(B2C and B2B). Considering the difficulty in obtaining
this valuable and guarded information from the e-commerce
sites, and the fact the sites we have considered
are quite busy, the results, although preliminary, could be
valuable for future studies on e-commerce workload characterization
and server designs.
The rest of the paper is organized as follows. Related
work is outlined in Section 2. Section 3 discusses the architecture
of e-commerce sites along with a description of the
configuration of the sites used for this study. Section 4 discusses
the behavior of the workload and the traffic and load
characteristics of the front-end and back-end servers. The
concluding remarks are sketched in Section 5.
Note: For this study data from two popular sites (one
B2C and the other B2B) was used. Due to a non-disclosure
agreement (NDA), the identity of these sites is not revealed.
Throughout this work the two sites are identified as B2C site
and B2B site. Without the NDA, we would not have been
able to acquire the data for the study.
Related Work
Although there have been several studies reported on the
workload characetrization of general web servers [3, 7, 10,
13], only a few studies have been reported on the characterization
of e-commerce traffic based on the client behavior.
The main reason for this shortcoming is the unavailability
of representative data. E-commerce sites have highly secure
information in the traces and access logs. Due to the
security implications e-commerce sites are reluctant to divulge
this information for research purposes. Due to this,
studies in this field are still in the preliminary stages. In [8]
the authors have developed a resource utilization model for
a server which represents the behavior of groups of users
based on their usage of the site.
It should also be noted that the existing work reported on
e-commerce traffic has been done on the front-end servers
only and to the best of our knowledge nothing has been reported
on the back-end servers. The back-end servers are the
ones which experience the maximum load in an e-commerce
environment [4]. We would like to characterize the load on
the back-end servers along with a study of the system characteristics
collected from system logs in E-commerce sites.
3 E-Commerce Architecture
A generic organization of e-commerce sites is depicted in

Figure

1. E-commerce sites can be broadly classified into
two different categories. Business to Business (B2B) and
Business to Consumer (B2C). The main difference between
the two categories of sites lies in the user population accessing
these sites. B2B sites serve transactions between different
businesses whereas B2C sites serve general users over
the Internet.
INTERNET
USERS
Edge router/
ISP Provider
Cache m/cs
Load Balancer
Web Servers
Firewall
Database
Servers
Front-End
Back-End

Figure

1: A Generic E-Commerce Site.
Business-to-Business: One of the main characteristics of
this category of sites is the regularity in the arrival traffic.
It was observed that heavy traffic comes between 9am to
5pm, normal business hours. Regularity does not imply the
lack of heavy spikes in the traffic. There will be sustained
peak load on the system either due to seasonal effects or
due to the availability of different services at the site. These
sites can be categorized by the high amount of buying taking
place in them. It has been observed that the percentage of
transactions resulting in buying are very high compared to
those in B2C environment.
Business-to-Consumer: B2C servers are the normal e-commerce
sites where any user can get service. The security
involved in B2C site is only restricted to any financial
transactions involved, whereas in a B2B environment all the
transactions are normally done in secure mode. One implication
of this is that increased buying in a B2C environment
can throttle the system since the designed system does not
expect high percentage of buy transactions. Another important
characteristic of a B2C site is the very low tolerance
to delayed responses. This increases the need to make QoS
more important than providing absolute security for all the
transactions, hence security is reserved for transactions involving
buying.
3.1 Front-End and Back-End Servers
Typically the front-end servers are comprised of the web
server, application server, server load balancer, and the secure
socket layer (SSL) off-loader. Front-end web servers
serve requests from the clients and are the only authorized
hosts able to access the back-end database and application
services as necessary. The application servers are responsible
for the business logic services. The application server
will be the most heavily loaded server in the B2C envi-
ronment. This is due to the heavy traffic of dynamic and
secure requests arriving at the server. In a large scale e-commerce
site, there will be dedicated application servers,
alternatively these servers can be combined with the Web
Servers or the Database servers. Due to the heavy traffic
seen by e-commerce servers and also due to the availability
requirements, there will be a network of web servers instead
of a single monolithic server at the front-end. This
basically improves the scalability and fault-tolerance of the
server to any bursts of busy traffic. Load balancers help increase
the scalability of an e-commerce site. Load balancing
works by distributing user requests among a group of
servers that appear as single virtual server to the end user.
SSL is a user authentication protocol developed by Netscape
using RSA Data Security's encryption technology. Many
commerce transaction-oriented web sites that request credit
card or personal information use SSL. The SSL off-loader
typically decrypts all https requests arriving at the server.
The back-end servers mainly comprise of the database
servers and the firewall which would protect sensitive data
from being accessed by unauthorized clients. These firewalls
provide security services through connection con-
trol. They are predominantly used when protecting mission-critical
or sensitive data is of utmost importance. The
database servers reside in the back-end of the network and
house the data for e-commerce transactions as well as sensitive
customer information. This is commonly referred to
as the data services. The clients do not directly connect to
these servers, the front-end Web servers initiate connections
to these servers when a client conducts a series of actions
such as logging in, checking inventory, or placing an order.
Most e-commerce sites scale up their database servers for
scalability and implement fail-over clustering for high avail-
ability. Partitioned databases, where segments of data are
stored on separate database servers, are also used to enhance
scalability and high availability in a scale-out fashion.
3.2 B2C Configuration
A simplified configuration of the B2C site being used for
the study is given in Figure 2. This site comprises of ten
web servers, each one powered by a Intel Quad P-III systems
with a 512MB of RAM. The web servers run IIS 4.0
HTTP server. This cluster of web servers is supported by
three image servers, each one powered by a Dual P-II sys-
tem. As can be seen from the figure, the image servers serve
both the database servers and the front-end web servers. For
the purpose of our study, the image servers were considered
to be in the back-end system. The product catalog server,
connected to both the front-end and the back-end, runs an
NT 4.0 providing backup and SMTP services to the back-end
servers. The LDAP server is connected to the back-end.

Figure

2: Simplified configuration of the B2C site.
3.3 B2B Configuration
In the B2B space, the design of e-commerce sites is completely
different from their design in B2C space. Here the
user population is known a-priori. The transactions being
processed by each user arriving at the server is also known
with reasonable bounds. B2B sites serve a limited population
as opposed to B2C sites which aim at serving the entire
Internet. These aspects enable the designers to customize
the site to specific user requirements.
Scalability is one of the main issue that has to be taken
care of when designing such customized system. So the design
is done as a cluster of B2C sites, interconnected to form
a large B2B portal. The interconnections between the individual
B2C components in the site determine the user population
to that site and also the services provided by that site.

Figure

3 shows a simplified version of the B2B site being
used for the study. Each of the web servers, can be individually
used as a B2C site with its own database and network
connection.

Figure

3: Simplified configuration of the B2B site.
4 Workload Characterization
In this study we have analyzed the behavior of e-commerce
servers with relation to the behavior of the incoming traf-
fic. Data was collected at different levels in the system.
Web Server access logs from the the front-end and the back-end
servers were collected at a granularity of 1 sec. This is
an application level data giving the load on the httpd. This
data will give the characteristics of the traffic arriving at the
system, average network bandwidth utilization, and the file
transfer rate.
For the system level information, data was collected from
the Performance logs from all the servers present in the site.
This data was collected at a granularity of 5 sec. This would
give information about the I/O bandwidth used, the processor
and disk utilization of the system etc. Data was collected
at a constant rate of 5 sec intervals. So this data is at
a higher scale than the logs from the web servers. But both
the scales are below the non-stationarity time scale used for
the analysis.
Data was collected at the server and the performance
monitor for an entire day. A weekday is used for data collection
since this would represent the average traffic. Addition-
ally, data for a five day period was used to study the average
behavior of the traffic over a long period of time.
4.1 Characteristics of the Workload
The main differences between general web and e-commerce
workload are the following.
1. Presence of a high level of Online Transaction Processing
(OLTP) activity is observed among the transactions
at the server. This is due to the database transactions
accruing for every request from the user. Due to security
reasons most of the data is present in the database
server which is protected by a secure firewall. This prevents
the web server from responding to most of the requests
without sending a query to the back-end server.
2. A large proportion of requests come in secure mode.
B2C traffic has lesser secure traffic, B2B sites experience
almost complete secure traffic from users. This
is due to the heavy security constraints present in industry
to industry transactions. Increased amount of
Arrival
Rate
(Reqs/sec)
Time

Figure

4: Arrival Process at B2C site.51525
Arrival
Rate
(Req/sec)
Time (Bucket 6 secs)

Figure

5: Arrival process at B2B site.
secure transactions implies heavy processing load at
the front-end server. Most of the sites have SSL off-
loaders, which do encryption/decryption of requests to
reduce the load on the system. This process adds to
the response time. Aggregating these transactions with
normal transactions increases the variability in the response
times observed by the user.
3. The proportion of dynamic requests (that require some
amount of processing) is very high, as was expected. In
fact, in most e-commerce sites almost all requests are
handled as dynamic requests.
4.2 Front-End Characterization
A visual inspection reveals the workload at e-commerce
sites to be more bursty than normal web workload. To study
this behavior, the following parameters were used, which
would have the maximum impact on the behavior of the traf-
fic: Arrival process, Utilization of the server, Response time,
Request file sizes, and Response file sizes.
4.2.1 Arrival Process

Figures

4 and 5 show the arrival process at the B2C and
e-commerce sites for an entire day (12am-12am). The
data shows traffic on a normal weekday with an average arrival
rate of 0.65 requests/sec at the front-end web server
for the B2C site and around 1 req/sec arrival rate at one of
the web servers in the B2B site. A visual inspection reveals
the burstiness in the arrival process. The B2C server is a
4P system with an average processor utilization of 6% per
processor and disk utilization of 2% during the period starting
from 9.00am till 6.00pm. The low utilization is typical
of e-commerce sites since they are designed for much higher
load and sustain a very minimal load during normal working
periods. It is the high load periods showing bursts of orders
of magnitude more than normal operating parameters which
cause concern for better capacity planning and performance
analysis of these systems.

Figures

4 and 5 show that the sites have distinct high
and low load periods during the course of a day. For the
B2C site, busy period starts around 6:00pm in the evening
and ends at around 11:00pm in the night. Since this is a
serving general consumers, the traffic is heavy during
the after-office periods. Distinctive low periods during
the morning between 7:30am to 11:30am can also be ob-
served. In case of the B2B site, the traffic concentration
lies mostly during normal office hours, between 9:00am to
8:00pm, which is intuitive. It should be noticed that the
graphs show aggregated arrival traffic for the B2B site and
the averaged arrival process for the B2C site.
The Arby-Veitch (AV) [11] estimator test was used for
estimating the Hurst-parameter (H-parameter) [6] for the arrival
time-series. This is known to be a reliable test for workloads
with busy periods showing a non-stationary behavior.
Hurst parameter is also calculated using the R/S plot test [6].
Reliability of this test under low time-scales for e-commerce
traffic is tested by comparing the H-parameters obtained using
the two methods.
The H-parameter is estimated to be 0.662 using the AV
test. This shows that the arrival process at the B2C site is
self-similar in nature. The Hurst parameter is also estimated
to be 0.662 using the R/S plot test for the B2C site, which
matches the estimation made by the AV-estimator. Similar
test was done for the arrival traffic at the B2B site. Using
the AV-estimator the H-parameter was estimated at 0.69,
whereas the R/S plot gave an estimate of 0.70 for the H-
parameter, which is a good approximation.
4.2.2 Processor Utilization

Figures

6 and 7 show the utilization of the front-end web
server for the B2C and B2B sites respectively. As explained
earlier, this data is collected between 9:00am till 5:00pm at
a granularity of 5 secs for the B2C site. For the B2B site the
data represents the activity between 10:00 am in the morning
till 9:30 am the next day morning. The B2C server sustains
a constant load throughout the day, with an average load of
7% on each of the four processors. High and low load periods
can be observed on the B2C server during the course
of the day. This behavior is absent in the B2B server. This
is due to the a-priori knowledge of the transactions and load
from users in the B2B space. B2B sites are customized for
specific traffic patterns and a normal traffic would not affect
the load on the system to a higher degree. Thus the load
on the system appears almost constant even though there is
a variation in the arrival rate at the server. The time-series
obtained from the utilization was also tested for self-similar
behavior. The AV-wavelet based test and the R/S plot test
are used for estimating the H-parameter. The estimated H-
parameter is 0.755 using the AV estimator, and 0.77 using
Utilization
(4P)
Time (secs)

Figure

Utilization of the front-end web-server
at B2C site10305070
Utilization
Time (buckets 5 secs)

Figure

7: Processor(2P) Utilization of one of the front-end
web-servers at B2B site
the R/S plot test for the B2C site. In the B2B space, the load
on the system did not have a high degree of self-similarity.
The H-parameter is estimated to be 0.66 using both the AV-
estimator and the R/S plot test. Due to a balanced load on
the B2B system throughout the duration, the degree of self-similarity
is very low. The effect of the arrival process is not
seen in the overall load sustained by the B2B server.
A higher H-parameter implies an increased degree of self-
similarity. Utilization is a factor of the response-time and
the arrival process. The inherent burstiness in the arrival
process is already established in the previous section. The
higher H value can be attributed to long-range dependence
in the service process.
4.2.3 Response Time
In

Figure

8, the response time observed by the users over
the entire day period is shown for the B2C site. Previous
studies [5, 12] have concentrated on the study of the heavy-tailed
behavior of web response times. In this work the
response-time distribution is converted into a time-series by
aggregating the response-times seen for non-overlapping intervals
of 5 secs. Even though the times seen are not the
actual response times observed by the user, they can be used
for time-series analysis. Only a multiplicative factor of 1/5
will be required to get the actual response-times. The time-series
obtained is checked for self-similarity and any non-stationary
behavior. The AV test and R/S plot test are used
for estimating the H-parameter (A-V test
As explained earlier, a good estimation of2000006000001e+061.4e+060 2000 4000 6000 8000 10000 12000 14000 16000 18000
Aggregate
Response
Time
Time (5 sec buckets)
response time

Figure

8: Aggregate Response time at the front-end Web-Server
(4P), B2C site
H-parameter is obtained using R/S test, only when the time-series
is stationary. So both the tests are used for estimating
the H-parameter.
Response time is one of the very important performance
metrics in the design and analysis of any server system.
High burstiness in the arrival traffic implies saturating server
queues, leading to high response times. Studies have shown
that the 90th percentile response-times can be used for predicting
the mean response-time [2]. This measure cannot
be used in presence of high burstiness in the response-time
distribution. Figure 8 shows response times orders of magnitude
higher during the high load periods in the evening.
Comparing this graph with the arrival process shown in Figure
4, unmistakable correlation can be found between the
different load periods. Even though the utilization of the
system does not get effected, buffer queue lengths increase
thereby increasing the user perceived response times. Increased
burstiness impacts the overall response time of the
system to a higher extent than the arrival process. This
burstiness in the response time is a factor of the back-end
data retrieval time and the server processing time.
4.2.4 Request/Response File Sizes
The request and response file sizes in web environment [5, 9]
have been studied previously. It was observed that these distributions
show a heavy-tailed behavior with a tail weight
of approximately
bytes [5]. This was considered one of the main reasons for
the heavy-tailed behavior of the web response times. In
e-commerce environment, it has already been shown that
transfer times have a heavy tailed behavior with In
this section the behavior of transfer size distribution is studied

Figures

show the request and response size
distribution over the observation period at the B2C server.
It can be observed that the distribution of transfer sizes is
fairly constant in the B2C environment. A visual inspection
rules out the possibility of heavy burstiness in the aggregated
time-series obtained from the transfer sizes. The distribution
of request sizes is further investigated for heavy-tailed behavior
using Log-Log cumulative distribution plots (LLCD)
plots [5].

Figure

11 shows the log-scale plot of the cumulative
probability function over the different request sizes
observed. The plot appears linear after x > 2:5. A linear-regression
fit to the points for requests more than 320 Bytes
Request
Size
(Bytes)
Requests arrival

Figure

9: Request size distribution over time, B2C site20000600001000001400001800000 10000 20000 30000 40000 50000
Response
Size
(Bytes)
Time (secs)

Figure

10: Response size distribution over time, B2C site
gives a line with slope This gives
an estimate of = 4.12 thereby indicating that the request
size distribution is not heavy-tailed in nature. This result refutes
the previous results about web traffic. In [5] the authors
found that the requests also follow a heavy tail distribution
with Using similar tests, we also infer that the
response file sizes do not follow a heavy tailed distribution.
4.3 Performance Implications
Previous studies on web traffic and LAN traffic have attributed
the self-similar behavior of network traffic to the
aggregation of long-range dependent ON/OFF processes.
In e-commerce space, the response-times are found to be
heavy-tailed in nature even though the request and response
file sizes are almost a constant. The heavy-tailed behavior
of response-times in web environment was believed to
be caused by the heavy-tailed behavior of the file transfer
sizes in the web environment. In e-commerce environment,
the transfer sizes do not follow a heavy-tailed distribution
as shown earlier in this section. Heavy-tailed behavior of
web transfer sizes are fundamentally caused by the inclusion
of image and video files in the overall traffic. Since
these files are minimized in e-commerce environment (for
reducing the overhead in response times), the behavior of
the transfer sizes becomes somewhat intuitive. The lack of
large image and video files removes the heavy-tailed nature
of e-commerce traffic.
It is observed that the response time still shows a heavy-tailed
behavior in both B2C and B2B space. As explained
earlier this implies that the user perceived response-time can
increase by orders of magnitude under load conditions. Due
Log10(Request file size)

Figure

11: LLCD of request size distribution, B2C site
to the critical nature of e-commerce applications and also the
business model (increasing criticality with the increase in
load), it is imperative that the response-times are kept under
normal bounds even in high load conditions. In e-commerce
environment response-time is mainly dependent on the processing
time and the transfer time. Since the file-sizes do
not follow a heavy-tailed distribution, it can be safely assumed
that the transfer time does not contribute to the variation
in the response-time. This shows that the characteristic
of the processing time is affecting the response-time to a
higher extent than the response size. Also the effect of file-
sizes appears to be negligible on the end-end response-times
observed. This result contradicts the behavior of response-times
for normal web traffic where the response-size of files
can be assumed as a good approximation of the response-
time. The difference is that, in web environment the transfer
times consumes most portion of the response-time which is
not the case in e-commerce environment due to the different
composition of requests.
4.4 Back-End Characterization
The most important and sensitive information in e-commerce
servers is kept in the back-end servers. It is the
back-end servers that execute the business logic for the e-commerce
site and are hence the most crucial components
of any e-commerce server. The parameters used for doing
the characterization depend mostly on the configuration of
the site and the purpose of the individual components [13]
in the back-end. The composition of back-end servers is
closely dictated by the business model of the site. So different
parameters might be interesting for different sites. In
this study processor utilization and disk accesses are used
for studying the characteristics of the two sites.
In the B2C site there are four different servers at the back-
end. These are: Main database server, Customer database
server, Image server, and LDAP server. The image server
and LDAP server are not heavily loaded during the observation
period. There is a single burst of traffic to and from
these servers when the data is updated daily. This burst is
also seen in other back-end databases and will be discussed
in detail later in this chapter. The only servers that experience
a sustained load throughout the day are the customer
database and the main database. These two servers are used
for studying the characteristics of the back-end system.
Processor
Utilization
(4P)
Time (from 9:33:13)

Figure

12: Processor utilization of Catalog Server (5 secs),
Processor
Utilization
Time (from 9:33:13)

Figure

13: Processor utilization of the Main D/B server (5
secs), B2B site
4.4.1 Processor Utilization
In

Figures

12 and 13 the processor utilization of the two
back-end servers in the B2C site is shown. It can be observed
that the back-end server experiences a sustained load
of 10% on average over the entire period. There is a visible
peak of almost 100% utilization of the catalog server.
This will be discussed later in the section. For the Main D/B
server, the utilization remains at around 30% for most of the
observation period. This shows that the load on back-end
servers is higher than on the front-end servers, when compared
with figure 6. Previous studies have speculated that
the load on the back-end servers is more regulated due to the
presence of the front-end server. One of the reasons for this
speculation is the service time of the front-end server. This
either causes a delay or reduces peak of any burst reaching
the back-end servers. This behavior of the back-end servers
is investigated by looking at the time-series obtained from
the utilization of the servers.
H-parameter values of 0.87 and 0.77 were obtained for
the utilization of the main database server and the catalog
server respectively. The burstiness observed at the back-end
servers is more than the front-end servers
results have been observed in the B2B space also. The
utilization of the database server of the B2B site is shown
in

Figure

14. It can be observed that the load on the system
reaches 100% around the 4000th bucket. This is due
to the update activity which takes place periodically in most
e-commerce sites. The actual time when this takes place is
around 1.00 pm in the night. Similar activity can be seen
in the other back-end servers. Nothing can be observed at10305070900 1000 2000 3000 4000 5000
Utilization
Time (bucket 1 sec)

Figure

14: Processor Utilization of the B2B Database server1000300050000 1000 2000 3000 4000 5000 6000
File
Operations
sec
Time (5 secs)

Figure

15: File Operations per second from Main DB server
(5sec)
the front-end servers, as the bulk of the data which needs
any maintenance is present in the back-end servers only.
The back-end server in B2B space is also found to be more
bursty than the front-end server. This contradicts previous
assumptions about burstiness at the back-end servers in web
environment.
4.4.2 Disk Accesses
The B2C site has four disks for the Main D/B system. Disk
are used for the study instead of disk utilization.
Reliable data could not be obtained for the disk utilization
due to the presence of a cluster of four disks.

Figures

15, shows the distribution of the file request rate
at the Main D/B server. This shows the arrival rate of file
requests seen by the four hard disks. Figure 16 shows the1030500 1000 2000 3000 4000 5000 6000
Avg.
Queue
Length
Time (5 secs)

Figure

Queue Length at Main DB server (5sec)
average queue length seen by the hard disks at the Main
D/B server. The average queue length is found to be self-similar
in nature with This would result in a
heavy-tailed behavior in the average response-time of the
hard disk. The reason for the burstiness in the queue length
can be attributed to the arrival of file transfers at the hard
disk. This rate is also found to be bursty in nature with H
0.83. The buffer cache does not appear to be effective
since the hard disk is experiencing requests at this level of
burstiness.
In the previous section, the response-time at the front-end
is found to be heavy-tailed in nature even though the request
and response size did not follow this distribution. The burstiness
in the service time at the back-end was attributed to this
behavior. Here it can be seen that the heavy-tailed distribution
of response-time at the back-end is due to the bursty
arrival process to the hard disks, causing the queue length
to be bursty. This high burstiness in queue length will remove
the effect file sizes may have on the transfer times.
This conclusion also supports the previous speculation that
file-sizes were not a good representation of response-times
in e-commerce environment.
5 Conclusion
Aggregated traffic arriving at B2B and B2C e-commerce
servers is characterized in this paper. Access logs from the
web servers is collected for application level information,
Microsoft performance logs were collected for system level
information and processor counters were collected for architectural
information. Information from this data was used
to understand the load behavior of the traffic for a normal
weekday. Only a specific set of parameters (arrival pro-
cess, utilization, response-time, transfer sizes etc.) which
would impact the system to the maximum extent were used
for characterization of the workload.
Self-similar nature of the traffic was established using
Hurst-parameter as a measure of degree of self-similarity.
Two different tests were used for measuring the Hurst-
parameter: AV-estimator and the R/S plot. It was observed
that the load behavior of the two sites was complementary
in nature with traffic load shifting from one type of e-commerce
site to the other during the later part of the day.
Unlike previous speculation, the back-end server was found
more bursty than the front-end server, this was attributed to
the fractal nature of the service time at the back-end. At both
the sites, the response-times were found to be heavy-tailed
in nature, complying to the results found in web environ-
ment. But in the B2C environment, highly bursty arrival of
file requests was seen at the disks. It was found that this
arrival process is causing high queuing delays at the disk reducing
the impact of disk transfer time as compared to the
queuing time. This increased the burstiness in the overall
response-time seen at the front-end server.
This work provides an understanding of the complexity
of the traffic arriving at e-commerce sites while providing a
preliminary workload characterization.



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--TR

--CTR
Lance Titchkosky , Martin Arlitt , Carey Williamson, A performance comparison of dynamic Web technologies, ACM SIGMETRICS Performance Evaluation Review, v.31 n.3, p.2-11, December
business-to-consumers B2C;self-similarity and web-servers;bussines-to-business B2B;traffic characterization;e-commerce servers