Lecture#10

Concluding session, part II

The Bonch-Bruevich Saint-Petersburg State University of Telecommunications

Series of lectures “Telecommunication networks”

Instructor: Prof. Nikolay Sokolov, e-mail: sokolov@niits.ru

New problems concerning throughputLast century:

We had to have 3.4 kHz for telephony (F1), 15 kHz for sound broadcasting (F2), and 8 MHz for TV broadcasting (F3).

So, total bandwidth with N1 channels for telephony, N2 channels kHz for sound broadcasting, and N3 channels for TV broadcasting can be calculated by the following formula:

N1xF1+ N2xF2+ N3xF3.

Current century:

We have to have 64 kbit/s for telephony (B1), from 64 kbit/s to 2 Mbit/s sound broadcasting (B2), from 2 Mbit/s to 30 Mbit/s for TV broadcasting (B3), and from from 2 Mbit/s to 100 Mbit/s for data transmission (B4).

Definitions related to QoS

In the Recommendation E.800 and in a number of other ITU-T documents several similar definitions of the term "Quality of service" are formulated: •1. Totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied of the user of the service (E.800).•2. The collective effect of service performance which determine the degree of satisfaction of a user of a service. It is characterised by the combined aspects of performance factors applicable to all services, such as; - Service operability performance; - Service accessibility performance; - Service retain ability performance; - Service integrity performance; and - Other factors specific to each service (Q.1741).•3. The collective effect of service performances which determine the degree of satisfaction of a user of the service (Y.101).•4. The collective effect of service performance which determine the degree of satisfaction of a user of a service. It is characterized by the combined aspects of performance factors applicable to all services, such as bandwidth, latency, jitter, traffic loss, etc (Q.1703).

Recommendation ITU-T E.800 (1)

Recommendation ITU-T E.800 (2)

Recommendation ITU-T E.800 (3)

Recommendation ITU-T E.800 (4)

Quality of service in PSTN

PSTN

Loss probability, mean delay, noise, etc.

Provider A Provider B Provider C

Loss probability, mean delay, noise, etc.

Loss probability, mean delay, noise, etc.

Problem of the transition to NGN

PSTN Operators should find viable strategy of the transition to NGN, which provides protection of investments in circuit-switched technology.

Source: B. Jacobs. Economics of NGN deployment scenarios: discussions of migration strategies for voice carriers. – www.ieee.org.

It is necessary to combine PSTN’s quality of service and IP technologies’ economic efficiency!

QoS aspect: time irreversibility

Speech quality impairment compensation in networks with circuit switching:

Elaboration of the new speech signal processing algorithms;

Signal amplification (when necessary).

Speech quality impairment compensation in IP networks under condition of the excessive packet transfer delay:

Impossible in principle!!!

Forecast (XV century)

“The time will come when people from the most distant countries will speak to one another and answer one another”.

Leonardo da Vinci

“Inaccurate” Predictions

“This ‘telephone’ has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us”

Western Union internal memo, 1876

“I think there is a world market for maybe 5 computers” Thomas Watson, Chairman of IBM, 1943

“There is no reason anyone would want a computer in their home ”

Ken Olson, President, Chairman & Founder Digital Equipment Corporation, 1977

“640K ought to be enough memory for anybody” Bill Gates, Microsoft, 1981

Conclusion: “Prediction is very difficult, especially if it's about the future“. (Niels Bohr, Nobel Prize in Physics in 1922.).

Two examples of trend extrapolations

Time

Extrapolation of Trends

tr t0 tf

F2(t)

F1(t)

Trends for functions F1(t), F2(t)

Forecasting of the future and the past

Year

Behavior of the investigated process for three ensembles

0 1 2 3 4 5 6 7 8 9 10 11 12 13

{X1} {X2} {X3}

F(t)

Forecasting related to access networks

Narrow band lines

Only mobile

Only broadband

Number of households, millions

40,0

30,0

20,0

10,0

0,0

2002 2007 2012

60%

20%

Year

Jipp curve (1)Jipp curve is a term for a graph plotting the number (density) of telephones against wealth as measured by the Gross Domestic Product (GDP) per capita. The Jipp curve shows across countries that teledensity increases with an increase in wealth or economic development (positive correlation), especially beyond a certain income. In other words, a country's telephone penetration is proportional to its population's buying power. The relationship is sometimes also termed Jipp Law or Jipp's Law.The Jipp curve has been called "probably the most familiar diagram in the economics of telecommunications". The curve is named after A. Jipp, who was one of the first researchers to publish about the relationship in 1963.The number of telephones was traditionally measured by the number of landlines, but more recently, mobile phones have been used for the graphs as well. It has even been argued that the Jipp curve (or rather its measures) should be adjusted for countries where mobile phones are more common that landlines, namely for developing countries in Africa.

Jipp curve (2)

Jipp curve (3)

Classifications of clients (1)

20%

20%

20%

20%

20%

Income share Portion of clients

Х1%

Х2%

Х3%

Х4%

Х5%

a) Ranking of clients by the level of income b) Ranking of clients by the time of the service using

Portion of clients

Time

100%

Innovators

Early adopters

Early majority

Late majority

Laggards

Classifications of clients (2)

Source: Telcordia Technologies

NGN as economical solution Increase of communication Operator’s revenues is possible by solving of two important problems. Firstly, independently or with assistance of services Providers, it is expedient to take over another niche, implicitly related to telecommunications business. The cases in point are information services which, in the long run will provide increase of Operators’ revenues. Secondly, revenues increase can be achieved when minimizing expenses. In this instance the matter concerns optimal ways of infocommunication system development and perfecting of maintenance processes. Efficiency of these processes determines, to a great extent, the level of Operational expenses on the system management. NGN concept – from the economic point of view can be considered as fulfilment of new requirements of potential clients at the expense of comparatively slight increase of CAPEX with essential decrease of OPEX.

Net present value (NPV)

Network modernization(Investment process)

CFin(t) CFout(t)

This index allows finding the correlation between investments and future income. Cash Flow on the input ( )inCF t is directed towards network modernization, which can be considered an investment project. As a result, output flow ( )outCF t is generated.

Examples of sensitivity analysis

x

NPV

x0xMIN xMAX

y

NPV

y0yMIN yMAX

Example of NPV (1) NPV(t)

t

Network creation

Network implementation

mod

erni

zati

on

Payback period

Example of NPV (2)

Source: ITU

Typical network planning tasks

Network planning processes

Three kinds of planning

Technical, business and operational plans

Change of the network structure during the time period

Time

CO1

CO2

CO3

CO4

CO5

CO6T1 T2

CO1

CO2

CO3

District 1

District 2

District 3

Two variants of operating network structure modification

CO1

CO2

CO3

District 1

District 2

District 3

CO1

CO2

CO3

District 1

District 2

District 3

CO1

CO2

CO3

District 1

District 2

District 3

The same network str

ucture

New network structure

CO4

CO5D

istr

ict 4

District 5

Access network modernization

Main distribution frame

Distribution cabinets

Line between cabinets

Mai

n ca

bles

Distribution cables

Distribution cables

X1

X2

Classification of the queueing models (1) In 1961 D.G. Kendall introduced the following notation for queueing models " / /A B n". Symbol A denotes arrival process, symbol B denotes service (holding) time distribution, and n indicates a number of servers. For a complete specification of a queueing system more information is required. For these reason Kendall's notation was extended:

/ / / / /A B n K S X , (14.1) where:

K is total capacity of the system, alternatively only the number of waiting positions,

S is number of customers, X is queueing discipline.

In the first position of classification (14.1) symbol M is used most often. This means, that incoming flow is a Poisson process. For more complicated models symbols GI (general independent time interval, renewal arrival process) and G (general, arbitrary distribution of time interval, may include correlation) are used.

Classification of the queueing models (2)

In the second position one of the following symbols is usually used: M (exponential distribution of service time), D (constant service time), kE (Erlang-k distribution of service time), nH (Hyper-exponential of order n distribution of service time), G (arbitrary distribution of service time). Occasionally other symbols occur. In a queueing system, demands can be served according to many different principles. Usually applied disciplines are as follows:

FCFS: first come – first served (it is also denoted as FIFO: first in – first out),

LCFS: last come – first served, SIRO: service in random order, SJF: shortest job first.

Classification of the queueing models (3) In some cases, demands are divided into N priority classes. There is difference of principle between two kinds of priorities: non-preemptive and preemptive. For the first discipline a new arriving demand with higher priority than a demand being served waits until a server becomes idle (and all demands with highest priority have been served). This discipline is also called HOL: head-of-the-line. When using second discipline a demand being served having lower priority than new arriving demand is interrupted. Usually three types of serving the interrupted call are discriminated:

1. preemptive, resume (the service is continued from where it was interrupted),

2. preemptive, without re-sampling (the service restarts from the beginning with the same service time),

3. preemptive, with re-sampling (the service restarts again with a new service time).

Main algorithm of the forecasting

Problem statement

Information gathering

Choice of methodology

Forecasting

Analysis of the results

Decision making

Usage of the forecast

Possible approaches

Another classification:

•objective methods (QUANTITATIVE FORECASTING METHODS)

•subjective methods (QUALITATIVE FORECASTING METHODS)

•combined methods

Considered objects

Network and its attributes

Services and corresponding traffic (some parameters)

QoS (including dependability)

Capacity of the trunks

Throughput of the switches

Forecasting and time

Main attributes

QoS parameters Services and traffic Throughput and

capacity

Short-term forecast Long-term forecastMedium-term forecast

Forecasting and lifetime

Lifetime of the access network

Lifetime of the switching equipment Post-NGN time

Lifetime of the terminals

Methods of the forecasting (1)

Genius forecasting – This method is based on a combination of intuition, insight, and luck. Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts are based exclusively on intuition. Science fiction writers have sometimes described new technologies with uncanny accuracy.There are many examples where men and women have been remarkable successful at predicting the future. There are also many examples of wrong forecasts. The weakness in genius forecasting is that its impossible to recognize a good forecast until the forecast has come to pass.Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream science generally ignores this fact because the implications are simply too difficult to accept. Our current understanding of reality is not adequate to explain this phenomena.

Methods of the forecasting (2a) Trend extrapolation – These methods examine trends and cycles in historical data, and then use mathematical techniques to extrapolate to the future. The assumption of all these techniques is that the forces responsible for creating the past, will continue to operate in the future. This is often a valid assumption when forecasting short term horizons, but it falls short when creating medium and long term forecasts. The further out we attempt to forecast, the less certain we become of the forecast. There are many mathematical models for forecasting trends and cycles. Choosing an appropriate model for a particular forecasting application depends on the historical data. The study of the historical data is called exploratory data analysis. Its purpose is to identify the trends and cycles in the data so that appropriate model can be chosen.

Methods of the forecasting (2b) The most common mathematical models involve various forms of weighted smoothing methods. Another type of model is known as decomposition. This technique mathematically separates the historical data into trend, seasonal and random components. A process known as a "turning point analysis" is used to produce forecasts. ARIMA models such as adaptive filtering and Box-Jenkins analysis constitute a third class of mathematical model, while simple linear regression and curve fitting is a fourth.The common feature of these mathematical models is that historical data is the only criteria for producing a forecast. One might think then, that if two people use the same model on the same data that the forecasts will also be the same, but this is not necessarily the case. Mathematical models involve smoothing constants, coefficients and other parameters that must decided by the forecaster. To a large degree, the choice of these parameters determines the forecast.

Methods of the forecasting (3a)

Consensus methods – Forecasting complex systems often involves seeking expert opinions from more than one person. Each is an expert in his own discipline, and it is through the synthesis of these opinions that a final forecast is obtained.One method of arriving at a consensus forecast would be to put all the experts in a room and let them "argue it out". This method falls short because the situation is often controlled by those individuals that have the best group interaction and persuasion skills.

Methods of the forecasting (3b)

A better method is known as the Delphi technique. This method seeks to rectify the problems of face-to-face confrontation in the group, so the responses and respondents remain anonymous. The classical technique proceeds in well-defined sequence. In the first round, the participants are asked to write their predictions. Their responses are collated and a copy is given to each of the participants. The participants are asked to comment on extreme views and to defend or modify their original opinion based on what the other participants have written. Again, the answers are collated and fed back to the participants. In the final round, participants are asked to reassess their original opinion in view of those presented by other participants.The Delphi method general produces a rapid narrowing of opinions. It provides more accurate forecasts than group discussions. Furthermore, a face-to-face discussion following the application of the Delphi method generally degrades accuracy.

Methods of the forecasting (4) Simulation methods – Simulation methods involve using analogs to model complex systems. These analogs can take on several forms. A mechanical analog might be a wind tunnel for modeling aircraft performance. An equation to predict an economic measure would be a mathematical analog. A metaphorical analog could involve using the growth of a bacteria colony to describe human population growth. Game analogs are used where the interactions of the players are symbolic of social interactions.Mathematical analogs are of particular importance to futures research. They have been extremely successful in many forecasting applications, especially in the physical sciences. In the social sciences however, their accuracy is somewhat diminished. The extraordinary complexity of social systems makes it difficult to include all the relevant factors in any model.

Methods of the forecasting (5)

Scenario – The scenario is a narrative forecast that describes a potential course of events. Like the cross-impact matrix method, it recognizes the interrelationships of system components. The scenario describes the impact on the other components and the system as a whole. It is a "script" for defining the particulars of an uncertain future. Scenarios consider events such as new technology, population shifts, and changing consumer preferences. Scenarios are written as long-term predictions of the future. A most likely scenario is usually written, along with at least one optimistic and one pessimistic scenario. The primary purpose of a scenario is to provoke thinking of decision makers who can then posture themselves for the fulfillment of the scenario(s). The three scenarios force decision makers to ask: 1) Can we survive the pessimistic scenario, 2) Are we happy with the most likely scenario, and 3) Are we ready to take advantage of the optimistic scenario?

Methods of the forecasting (6) Decision trees – Decision trees originally evolved as graphical devices to help illustrate the structural relationships between alternative choices. These trees were originally presented as a series of yes/no (dichotomous) choices. As our understanding of feedback loops improved, decision trees became more complex. Their structure became the foundation of computer flow charts. Computer technology has made it possible create very complex decision trees consisting of many subsystems and feedback loops. Decisions are no longer limited to dichotomies; they now involve assigning probabilities to the likelihood of any particular path. Decision theory is based on the concept that an expected value of a discrete variable can be calculated as the average value for that variable. The expected value is especially useful for decision makers because it represents the most likely value based on the probabilities of the distribution function.

Methods of the forecasting (7) Combining ForecastsIt seems clear that no forecasting technique is appropriate for all situations. There is substantial evidence to demonstrate that combining individual forecasts produces gains in forecasting accuracy. There is also evidence that adding quantitative forecasts to qualitative forecasts reduces accuracy. Research has not yet revealed the conditions or methods for the optimal combinations of forecasts. Judgmental forecasting usually involves combining forecasts from more than one source. Informed forecasting begins with a set of key assumptions and then uses a combination of historical data and expert opinions. Involved forecasting seeks the opinions of all those directly affected by the forecast (e.g., the sales force would be included in the forecasting process). These techniques generally produce higher quality forecasts than can be attained from a single source. Combining forecasts provides us with a way to compensate for deficiencies in a forecasting technique. By selecting complementary methods, the shortcomings of one technique can be offset by the advantages of another.

Example of Delphi technique

The question is: how many Y-terminals will be installed up to 2000 year?

1. Ten experts sent the following estimations: 1 million (5 opinions), 1.2 million (3 opinions), 1.4 million (2 opinions).

2. Mean value is

(1) 1.0 5 1.2 3 1.4 21.14

10N

3. Variance is2 2 2

2 (1.0 1.14) 5 (1.2 1.14) 3 (1.4 1.14) 20.0244

10σ

4. Coefficient of variation is (1)

0.137σ

kN

Conclusion: forecast is stable.

DependabilityStrictly speaking, dependability should be considered as one of quality aspects. Nevertheless, some specialists consider dependability as an independent term that has the same status as the quality. The dependability is the property of an object to retain, in a course of time, within specified limits values of all parameters, which characterize capability to perform required functions in predetermined for that object regimes and conditions of application, technical maintenance, repairs, storage and transportation. It is obvious, that there is no sense in speaking about object’s dependability during the time periods, when it is withdrawn from operation for execution of scheduled inspections, modernization and other procedures.

Dependability vs cost

Dependability and type of service

Statistics of the dependability

Intensity of failures

t

λ(t)

Sudden failures (infant

mortality region)

GradualFailures

(wear-out region)

t1 t2

λ(t) ≈ const

(steady-state region)

Dependability of the access network

Absence of call request signal

or ring signalBreak of the subscriber line 16%

12%

48%

24%

Noise

Signal about overloading

Source: ISO

Reservation of infocommunication system on the level of access network

Wireless access

PC

Wireline access

Base station

Core Network

Examples of dependability analysis

A=p+p2–p3

A=?

If p=0.999, then A=0.999998001

A=2p5–5p4+2p3+2p2

A=?

If p=0.999, then A=0.999997998

If p=0.9, then A=0.981

If p=0.9, then A=0.978

Comparison of the scenarios Criterion I

Criterion II

Criterion III

Criterion VI

Criterion V

Scenario 1

Scenario 2

Network modernisation

New concept

Modernisation

InvestigationElaboration of the

main solutionsProduction of

equipment

Network planning

Implementation of the concept

Technical maintenance

Network planning and data mining

Operational system development (e.g. network)

Forecasting (e.g. number of users)

Data mining

Information from feedback loops (e.g. statistics)

?

Instructor: Prof. Nikolay Sokolov, e-mail: sokolov@niits.ru

Questions?

Concluding session, part II