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Bandwidth Provisioning and Capacity Planning Tools
J. Almhanaa, Z. Liu
a, R. McGorman
b, B. Lanteigne
a
aUniversity de Moncton, bLodex International Consulting{almhanaj, Lanteib, liuz}@umoncton.ca
Abstract
Bandwidth provisioning is an important issue for
Internet service providers (ISPs) and ensuring quality of
service (QoS) is a major concern. QoS is closely related
to the available bandwidth which itself is subject to
financial constraints. Unfortunately, up to now there havebeen no adequate tools available in the market so that
ISPs can do accurate bandwidth provisioning. In thispaper, we describe the software tools developed by
GRETI1 for that purpose. These Tools provide several
calculators for voice over IP and data traffic.
Keywords: Network capacity planning, Internet traffic,
Voice/IP, Traffic modeling, Network Tools
1. Introduction
For Internet service providers, bandwidth provisioning
and ensuring QoS are important issues. Especially whenfacing vigorous competition, ISPs must provide
satisfactory service levels, but cost pressures mean
provisioning must be economical. An ISP can face severalscenarios where more bandwidth is needed, including
growth in subscribers and increased user demands for
more Internet applications, such as VoIP (voice overinternet protocol) and other data traffic. Because of the
lack of appropriate planning tools, ISPs sometimes simplydouble network bandwidth and, therefore, their
investment costs. This practice, amplified by overly
optimistic bandwidth growth projections, led to a
catastrophic economical impact in the last 5 years. Some
cable companies now have more bandwidth than the
market can consume. In other scenarios, ISPs want toincrease profits from their existing communication
networks by adding users or other services but they maybe unsure about how to maintain QoS. QoS is related to
user satisfaction, and can have an economic impact when
dissatisfied users switch to another ISP for better service.
Some other scenarios also exist, for example, an ISP mayneed to establish pricing based on various levels of qualityof service.
1Groupe de Recherche en Technologies Avances dInternet
We believe that provisioning tools will help ISPs to
better manage their networks and optimize their resources.
Unfortunately, to the best of our knowledge, there havebeen no adequate tools available for bandwidth
provisioning and network capacity planning for ISPs whooffer so-called triple play services on the same medium:
VoIP, TV and high speed internet access. Some existing
VoIP tools [8] and video streaming calculators [10] offer
partial or incomplete solutions to bandwidth provisioning,but these tools are not capable of handling general internet
data traffic. Data traffic constitutes a major component ofnetwork traffic and must be taken into account. The
objective of this paper is to describe new software tools
we developed for bandwidth provisioning and network
capacity planning for integrated VoIP and data services.
These tools are part of the Data Traffic Analysis and
Tools Development project [12]. Our tools contain severalcalculators for bandwidth provisioning for a mediumcontaining VoIP and general data traffic. In this short
paper, we focus only on the description of the tools
without going into the implementation details, our main
purpose is to introduce our tools and show its importance
for ISP. The related theoretical background andimplementation details are documented in [2, 3, 4, 5].
The rest of the paper is structured as follow: Section 2
briefly explores some theoretical background. Section 3
describes our tools in more detail. Section 4 compares
our tools with various tools already available. Section 5concludes the paper.
2. Brief theoretical background of the tools
Traffic modeling is the first step towards bandwidthprovisioning and network resource optimization. As a
result, telecommunication traffic modeling has received
considerable attention during the past decade. For the
integrated service IP networks that we are considering, the
traffic consists of voice, high speed Internet data, video
streaming and background traffic. In practice, the totalbandwidths are divided into several parts and are
allocated to the various services. This static scheme iseasy to implement but wastes a lot of bandwidth. In this
paper, we focus on bandwidth provisioning and QoS for
voice and data services.
Proceedings of the 4th Annual Communication Networks and Services Research Conference (CNSR06)
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2.1. Available Models for Bandwidth
Provisioning
With an appropriate model, it is possible to perform
calculations relating bandwidth, number of users, and
QoS. For this reason, models are necessary for creatingprovisioning tools. Two well known models for voicetraffic are: Poisson often used in the US, and Erlang B,
often used in Canada. The main difference in their outputs
is that Poisson provisions more channels than Erlang B,
thus providing a margin of safety but also increasing the
possibility of having unused channels. There is also anextension to the Erlang B model, simply known as
Extended Erlang B. The Extended Erlang B takes into
consideration the fact that a caller who is blocked due to
lack of resources may retry immediately, increasing the
traffic load. Models such as Poisson and Erlang B can be
adapted for VoIP. This is accomplished by first finding
the number of channels needed, and then translating thisinto a bandwidth requirement based on the codec being
used and other factors. Another possibility is to use a
Gaussian approximation to the Poisson model to find the
number of channels.
Voice traffic was well studied and understood. The
challenge lies in modelling data traffic. A significantfinding was that data traffic has long memory, namely, the
autocorrelation function decays hyperbolically rather than
exponentially [10]. This means that traditional teletraffic
models based on Poisson processes are not suitable for
high speed telecommunication network traffic modeling.On the other hand, since long range dependent processes
have infinite memory, the model performance analysis
becomes very complex. For this reason a lot ofapproximate models have been proposed, including the
Markov modulated Poisson process models, e.g., [1] and
the M/G/infinity model [7].
2.2. Models used in the tools
For the purpose of bandwidth provisioning, the traffic
model should be very simple but flexible enough to
capture various traffic patterns. Based on the central limit
theory, when the number of subscribers is large enough,the aggregated voice traffic is approximated by a
Gaussian distribution. For VoIP, our Bandwidth Capacity
planning tools use the Gaussian model to perform thecalculations relating bandwidth, number of users and
blocking probability to determine the one unknown
quantity. On the other hand, when it comes to data trafficmodeling, our research has shown potential in the Gammadistribution, as seen in [5]. One of the data trafficcalculators we implemented uses the Gamma distribution
to perform its calculations. The other two data traffic
calculators use the dimensioning formula described in [3].
3. Description of our tools
Our tools include seven calculators, for which Figure 1
shows the main interface. Due to space limits, we do not
include the interface of each individual calculator.
Figure 1. The main interface
The first two calculators are dedicated to VoIP. The
first, called Voice Over IP Calculator, computes
bandwidth, number of users and blocking probability (i.e.QoS) for homogenous voice traffic using a single type of
CODEC. The second, called Multi-Codecs Voice Over
IP Calculator does similar computations but forheterogeneous voice traffic generated from several user
groups with different CODECs.For data traffic we have two options. The first is based
on dimensioning formula [3], which is implemented in
calculators 3 and 4. The second uses an empirical model
based on the Gamma distribution, and is implemented in
calculator 5.
Using the dimensioning formula, calculator 3, called
Dimensioning HSDT2 Calculator, computes one of thethree quantities: number of users, bandwidth, and QoS.
Given any two of these, it computes the third. Calculator
4, called Multi-Groups Dimensioning HSDT Calculator,
is similar to the previous one but with it we can defineseveral user groups with different requirements.
Calculator 5, called Empirical HSDT Calculator,
performs similar calculations to those of calculator 3 butusing the Gamma model [5].
2HSDT: High Speed Data Traffic
Proceedings of the 4th Annual Communication Networks and Services Research Conference (CNSR06)
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Calculators 6 and 7 are called Voice and Data
Calculator and Multi-Groups Voice and DataCalculator, respectively. With them, bandwidth
provisioning is done for traffic containing both data and
voice. Dimensioning formula [3] is only used to model thedata traffic.
4. Comparisons with other software
To the best of our knowledge, there are no similar toolsavailable in the market with which we can compare ourtools, especially for data traffic. However certain voice
calculators like [8] perform similar computations to our
first calculator. Our second calculator, related to voice
traffic, goes beyond simple bandwidth calculations by
adding multiple codec groups. This makes it more useful
in practice.
5. Conclusion
In this paper, the motivation and brief descriptions ofour Capacity Planning tools were given. We believe that
such tools are needed by ISPs and would help them
achieve better bandwidth provisioning in a market wherevigorous competition is growing continuously.
Our tools are currently at the prototype stage of
development. Further tuning in cooperation with ISPs and
more investigation are presently undergoing. In the future
we are planning to add video streaming to our tools.
Acknowledgments
This work was jointly funded by ACOA and Nortel
through the AIF to Dr. J. Almhana, and by NSERC to Dr.
V. Choulakian.
References
[1] Allan T. Andersen and Bo Friis Nielsen, A
Markovian Approach for Modeling Packet Traffic with
Long-Range Dependance,IEEE Journal, June 1998, pp
719-732.
[2] J. Almhana, Z. Liu, V. Choulakian and R. McGorman,
A Recursive Algorithm for Gamma Mixture Models,Proc. of IEEE ICC 2006, Istanbul, 2006.
[3] J. Almhana, Z. Liu, V. Choulakian and R. McGorman,
IP Network Traffic Modeling and Capacity Planning,Internal Report, 2005.
[4] R. McGorman, J. Almhana, V. Choulakian, Z. Liu, W.
Jedidi, Similarities between Voice and High SpeedInternet Traffic Provisioning,Proceedings of CNSR2004.
Fredericton, May 19-21, 2004.
[5] R. McGorman et. al. Empirical Bandwidth
Provisioning Models for High Speed Internet Traffic,Proceedings of CNSR2006, Moncton, May 24-25, 2006.
[6] K. Park and W. Willinger, Self-similar network traffic
and performance evaluation, John Wiley and Sons, Inc.
New York, 2001
[7] M. M. Krunz and A.M. Makowski, Modeling video
traffic using M/G/infinity input processes: A compromise
between markovian and LRD models,IEEE J. Select.
Areas Commun., 1998, pp. 733-748.
[8] Voice/IP Calculators http://www.voip-
calculator.com/calculator/ , 23/07/2004
[9] VoIP Providers List, VoIP Providers List VoIP
Calculator, http://www.voipproviderslist.com/voip-
calculator.
[10] VSS, Video Streaming Hosting Calculator,http://www.videostreamingservices.com/Hosting_prices_
calculator.htm, 2003.
[11] W. Leland, M. Taqqu, W. Willinger and D. Wilson,On the self-similar nature of Ethernet traffic,Proc.
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[12] http://www.cnsr.info/research/componenth.php
Proceedings of the 4th Annual Communication Networks and Services Research Conference (CNSR06)
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