The workload of the global Internet is dominated by the
Hypertext Transfer Protocol (HTTP), an application protocol
used by World Wide Web clients and servers. Simulation
studies of IP networks will require a model of the
traSJic putterns of the World Wide Web, in order to investigate
the effects of this increasingly popular application.
We have developed an empirical model of network trafic
produced by HTTI? Instead of relying on server or client
logs, our approach is based on packet traces of HTTP
conversations. Through traffic analysis, we have determined
statistics and distributions for higher-level quantities
such as the size of HTTPJiles, the number ofjles per “Web page”, and user browsing behavior. These quantities
form a model can then be used by simulations to
mimic World Wide Web network applications.
Our model of HTTP traffic captures logically meaningful
parameters of Web client behavior, such as file sizes
and “think times”. The traffic traces described in the preceding
section provide us with empirical probability distributions
describing various components of this behavior.
It is used these distributions to determine a synthetic workload.
At the lowest level, our model deals with individual
HTTP transfers, each of which consists of a request-reply
pair of messages, sent over a single TCP connection. We
model both the request length and reply length of HTTP
At first glance, it may seem more appropriate for a
model of network traffic to deal with the number, size, and
interarrival times of TCP segments. However, we note that
It is shown that it is appropriate to model the first HTTP
transfer on a Web page separately from subsequent retrievals for that
page. For simplicity, we have postponed discussion of this distinction.
these quantities are governed by the TCP flow control and
congestion control algorithms. These algorithms depend in
part on the latency and effective bandwidth on the path
between the client and server. Since thi information cannot
be known a priori, an accurate packet-level network
simulation will depend on a simulation of the actual TCP
algorithms. This is in fact the approach taken for other
types of TCP bulk transfers in the traffic model described
in [lo]. In a similar fashion, our model generates transfers
which need to be run through TCP’s algorithms; it does
not generate packet sizes and interarrivals by itself.
A Web document can consist of multiple files. A
server and client may need to employ multiple HTTP
transactions, each of which requires a separate TCP connection,
to transfer a single document. For example, a document
could consist of HTML text 131, which in turn could
specify three images to be displayed “inline” in the body
of the document. Such a document would require four
TCP connections, each carrying one request and one reply.
The next higher level above individual files is naturally the
Web document, which we characterize in terms of the
number offiles needed to represent a document.
Between Web page retrievals, the user is generally
considering her next action. We admit the difficulty of
characterizing user behavior, due to its dependency on
human factors beyond the scope of this study.
we can model user think time based on our observations.
Assuming that users tend to access strings of documents
from the same server, we characterize the locality of
reference between different Web pages. We therefore
define the consecutive document retrievals distribution as
the number of consecutive pages that a user will retrieve
from a single Web server before moving to a new one!
Finally, the server selection distribution defines the
relative popularity of each Web server, in terms of how
likely it is that a particular server will be accessed for a set
of consecutive document retrievals.
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