WAP Traffic: Description and
Comparison to WWW Traffic

Thomas Kunz

Systems and Computer Engineering

Carleton University

http://kunz-pc.sce.carleton.ca/

tkunz@sce.carleton.ca

Overview

Overview of WAP (Wireless Application Protocol) and Trace Collection

The Big Picture

Sessions

Activity Factors

Conclusions and Future Work

WAP Forum

“de-facto world standard for wireless information and telephony services on digital mobile phones and other wireless terminals”

published a global wireless protocol specification based on existing Internet standards such as XML and IP for all wireless network

Marketing Cloud:

Handset manufacturers representing over 75% of the world market across all technologies have committed to shipping WAP-enabled devices.

Carriers representing nearly 100 million subscribers worldwide have joined WAP Forum

Trace Collection Infrastructure

The Big Picture:
Traffic Profile (June – December 1999)

The Big Picture:
Average Daily Traffic

Sessions

Users typically make a sequence of requests, defined as a “browser session”

A wireless channel is allocated at beginning and released at the end:

90 seconds timeout

User terminates browser application

Phone is powered off

Problems:

IP addresses assigned dynamically, so we cannot track users

When session times out, new IP address may be assigned, even though same “user session”

Number of sessions closely follows average daily traffic

Sessions are, on average, rather short (90% are less than 3.77 minutes)

Sessions: Average Number of Concurrent Sessions (June-Dec 1999)

Activity Factor

Wireless link is scarce resource, but is it well used by user/browser?

Activity Factor: percentage of time that channel is used to transmit data

Determination:

How long is channel allocated to user

Trivial for pure circuit-switched connection

More complicated for CDMA, where channel is not always at full rate

How much time is spent transferring user data

assuming link bandwidth of 19.2 kbps

data volume per session known from traces

Activity factors differ for uplink and downlink, but are constant for period studied

Uplink: 11%

Downlink: 30%

WAP vs. WWW Traffic

Some similarities:

Periodicity

Daily patterns (and to some extent half-daily patterns)

Weekly patterns (and to some extent half-weekly patterns)

Not enough trace data to confirm/test for seasonal patterns

Self-similarity (Hurst Parameter between 0.79 and 0.82)

But also some differences:

Smaller packets (95% of all packets less than 220 bytes)

Shorter sessions

Traffic more balanced (dowlink traffic “only” about 3 times as much data as uplink traffic)

No growth trend in data presented in paper, but long-term growth trend clearly visible since

Conclusions and Future Work

Installed trace collection infrastructure, continuous trace collection effort

Analyzed traffic, derived a number of properties, compared to WWW traffic

Future Work:

Reconfirm findings/invariants for traces collected since January 1, 2000

Refine analysis with additional data sources (can we match users to IP addresses over time?)

Use traces to build performance prediction models based on LQN (layered queuing models):

Impact of more users

New applications


	Thomas Kunz
	Systems and Computer Engineering
	Carleton University
	http://kunz-pc.sce.carleton.ca/
	tkunz@sce.carleton.ca


	Overview of WAP (Wireless Application Protocol) and Trace Collection
	The Big Picture
	Sessions
	Activity Factors
	Conclusions and Future Work


	“de-facto world standard for wireless information and telephony services on digital mobile phones and other wireless terminals”
	published a global wireless protocol specification based on existing Internet standards such as XML and IP for all wireless network
	Marketing Cloud:
		Handset manufacturers representing over 75% of the world market across all technologies have committed to shipping WAP-enabled devices.
		Carriers representing nearly 100 million subscribers worldwide have joined WAP Forum


	Users typically make a sequence of requests, defined as a “browser session”
	A wireless channel is allocated at beginning and released at the end:
		90 seconds timeout
		User terminates browser application
		Phone is powered off
	Problems:
		IP addresses assigned dynamically, so we cannot track users
		When session times out, new IP address may be assigned, even though same “user session”
	Number of sessions closely follows average daily traffic
	Sessions are, on average, rather short (90% are less than 3.77 minutes)


Wireless link is scarce resource, but is it well used by user/browser?
Activity Factor: percentage of time that channel is used to transmit data
Determination:
	How long is channel allocated to user
		Trivial for pure circuit-switched connection
		More complicated for CDMA, where channel is not always at full rate
	How much time is spent transferring user data
		assuming link bandwidth of 19.2 kbps
		data volume per session known from traces
Activity factors differ for uplink and downlink, but are constant for period studied
	Uplink: 11%
	Downlink: 30%


Some similarities:
	Periodicity
		Daily patterns (and to some extent half-daily patterns)
		Weekly patterns (and to some extent half-weekly patterns)
		Not enough trace data to confirm/test for seasonal patterns
	Self-similarity (Hurst Parameter between 0.79 and 0.82)
But also some differences:
	Smaller packets (95% of all packets less than 220 bytes)
	Shorter sessions
	Traffic more balanced (dowlink traffic “only” about 3 times as much data as uplink traffic)
	No growth trend in data presented in paper, but long-term growth trend clearly visible since


Installed trace collection infrastructure, continuous trace collection effort
Analyzed traffic, derived a number of properties, compared to WWW traffic
Future Work:
	Reconfirm findings/invariants for traces collected since January 1, 2000
	Refine analysis with additional data sources (can we match users to IP addresses over time?)
	Use traces to build performance prediction models based on LQN (layered queuing models):
		Impact of more users
		New applications