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Tutorial of Network design, Study Guides, Projects, Research of Network Design

Tpoics covered is the IEEE paper. It involves capacity planning

Typology: Study Guides, Projects, Research

2019/2020

Uploaded on 03/28/2020

sarah-bhalla
sarah-bhalla 🇮🇳

5 documents

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TUTORIAL NO. : 02
Name: Sarah Bhalla
Roll No. : J204
ABSTRACT:
In particular, we study the optimal decision problem of building new network capacity in the
presence of stochastic demand for services.
The existing telecommunications infrastructure in most of the world is adequate to deliver voice and
text applications, but demand for broadband services such as streaming video and large file transfer
(e.g. movies) is accelerating.
We notice that sometimes it is optimal to wait until the maximum capacity of a line is nearly reached
before upgrading directly to the line with the highest known transmission rate (skipping the
intermediate lines).
We study the underlying risk factor in the bandwidth market, and then apply real options theory to
the upgrade decision problem.
INTRODUCTION:
To the best of our knowledge, this real options approach has not been used previously in the area of
network capacity planning.
As an alternative, the real options approach, can be used to effectively model investment flexibility.
A number of publications discuss the use of real options theory for optimal investment timing, but
researchers in network planning and management do not appear to have used these concepts .
The outline of this paper is as follows: Section II describes the modelling framework; Section III
presents the mathematical model and introduces an upgrade decision algorithm; Section IV presents
the estimated model parameters; and Section V contains different simulated results.
The volatility present in the demand market for capacity requires the development of risk
management and investment decision systems.
However, a disk manufacturing plant can be profitable as long as demand increases rapidly enough
to offset falling prices due to technological improvements.
Consequently, the revenue to the owner of the network is determined by the prevailing price and
the amount used (demand for capacity).
In some cases, the fundamental factor driving profitability is the amount which can be sold, as
opposed to the price received per unit.
Relentless technological development decrease costs while demand increases exponentially.
In contrast, the price per bit per second is falling exponentially
The price per megabyte of disk drives has decreased exponentially.
We expect that as bandwidth market inefficiencies disappear, price and demand may both be
determining risky factors.
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TUTORIAL NO. : 02

Name: Sarah Bhalla Roll No. : J20 4

ABSTRACT:

In particular, we study the optimal decision problem of building new network capacity in the presence of stochastic demand for services. The existing telecommunications infrastructure in most of the world is adequate to deliver voice and text applications, but demand for broadband services such as streaming video and large file transfer (e.g. movies) is accelerating. We notice that sometimes it is optimal to wait until the maximum capacity of a line is nearly reached before upgrading directly to the line with the highest known transmission rate (skipping the intermediate lines). We study the underlying risk factor in the bandwidth market, and then apply real options theory to the upgrade decision problem. INTRODUCTION: To the best of our knowledge, this real options approach has not been used previously in the area of network capacity planning. As an alternative, the real options approach, can be used to effectively model investment flexibility. A number of publications discuss the use of real options theory for optimal investment timing, but researchers in network planning and management do not appear to have used these concepts. The outline of this paper is as follows: Section II describes the modelling framework; Section III presents the mathematical model and introduces an upgrade decision algorithm; Section IV presents the estimated model parameters; and Section V contains different simulated results. The volatility present in the demand market for capacity requires the development of risk management and investment decision systems. However, a disk manufacturing plant can be profitable as long as demand increases rapidly enough to offset falling prices due to technological improvements. Consequently, the revenue to the owner of the network is determined by the prevailing price and the amount used (demand for capacity). In some cases, the fundamental factor driving profitability is the amount which can be sold, as opposed to the price received per unit. Relentless technological development decrease costs while demand increases exponentially. In contrast, the price per bit per second is falling exponentially The price per megabyte of disk drives has decreased exponentially. We expect that as bandwidth market inefficiencies disappear, price and demand may both be determining risky factors.

The current inefficiencies in the bandwidth market can be explained by the fact that deregulation is recent and consumers are paying for capacity rather than paying for consumption. Consequently, we believe that as the bandwidth market becomes more efficient, contracts will be based on bandwidth spot prices. This situation is in contrast with traditional financial markets where price, and not demand, is the dominating factor (in other words, in financial markets it is almost always assumed that demand curves are perfectly elastic). To go back to our example of disk storage, the price per megabyte has decreased fairly smoothly over the last few years, while demand for storage media has been more uncertain. We note that a model for the bandwidth market which includes both price and quantity effects has been described in [7]. However, during the current period of transition, we do not believe that there is a liquid enough spot market to value contracts and investments based on these prices. In an efficient market, price should reflect the actual amount of bandwidth used by a consumer. MATHEMATICAL MODEL Conversely, if the stock price on June 1 is lower than $50 , the option becomes worthless: the investor would not use it to pay to own the stock when it could be purchased at a lower price on a financial exchange. If the price of that stock on June 1 is higher than $50, e.g. , then the option has turned out to be valuable since the investor can use it to purchase the stock for and then sell the stock immediately for a profit of. A financial option gives its holder the right, but not the obligation, to trade (buy or sell) at a future time for a specified price. If so, the manager will compare the exercise price of the option to the current price of the bundle. The field of option pricing is largely concerned with determining the fair price to pay for options. For example, a network planning manager may buy an option on a bundle of dark fiber lines. A strike price: the price at which one party has agreed to pay the other party should the option be exercised. ESTIMATION OF PARAMETER To estimate the uncertainty parameter, we use data from the University of Waterloo campus network .To filter out this effect, we average the network traffic for the entire period for each day of the week separately, and choose the day with the highest average total daily traffic. In particular, to estimate we use summary traffic data for each academic term dating back to 1997. TELECOM MARKET PRICE OF RISK ESTIMATION