Mạng và viễn thông P31

These methods in various ways served to measure, over a predetermined period of time, any of the following parameters 0 the total number of calls or messages 0 the total conversation t

31

Trafic Monitoring and

Forecasting

Having explained the underlying principles of electrical communication and the statistical ‘laws’

of telecommunications traffic, we can now consider the practical design and operation of

networks A prime concern is to ensure that there are adequate resources to meet the traffic demand, or to prioritize the use of resources when shortfalls are unavoidable Two things have to

be done to keep abreast of demand These activities are the monitoring and future forecasting of network use In this chapter we shall review the parameters to be applied in measuring traffic activity and go on to provide an overview of some forecasting models for the prediction of future demand Used as an input to the teletraffic engineering formulae of Chapter 30, the forecast values of future traffic can be used to predict the future network equipment requirements on which the overall planning process will be based

31.1 MEASURING NETWORK USAGE

We discussed different methods of defining the volume of network usage in Chapter 30 These methods in various ways served to measure, over a predetermined period of time, any of the following parameters

0 the total number of calls or messages

0 the total conversation time

0 the total holding time (holding time includes both conversation time and call set-up time)

0 the total number of data characters (e.g packets, frames or cells conveyed)

We have covered the concept of trafic intensity, which is a measure of the average call

demand We have shown how traffic intensity measured in Erlangs determines the

number of circuits needed on a route in a circuit-switched network to maintain a given

grade of service For this reason it is usual practice to monitor the magnitude of the

555

Networks and Telecommunications: Design and Operation, Second Edition.

Martin P Clark Copyright © 1991, 1997 John Wiley & Sons Ltd ISBNs: 0-471-97346-7 (Hardback); 0-470-84158-3 (Electronic)

traffic intensity, and to predict its future values by forward forecasting This provides the basis for planning a future circuit provision programme

Forecasting, however, need not be confined to predicting future traffic intensity The measuring and forecasting of other parameters can also be valuable predictors of network traffic demand For example, in telephone networks where customers pay for calls based on the total time used in conversation, the measurement of conversation

time not only allows the preparation of customer bills, but also enables the network operator to calculate the revenue due and the resulting profits, immediately after the end of each financial period Meanwhile, packet data network customers usually pay

for the total volume of data carried (measured in segments) The actual amount of use

and predicted future revenue may well be vital to the network operator on the business and financial side

Forecasting also allows investments to be planned to meet future demand,

commensurate with predicted levels of profits and financial resources Forecasting also helps to determine when established will become unprofitable, and therefore when it may be appropriate to consider run-down or discontinuation of the service No com- mercially-minded company can put up with loss-making products or services for long

The parameters commonly used to monitor the usage of circuit-switched or point-to- point (leased) networks are listed below

0 the traffic intensity

0 the total number of minutes of usage

0 the total number of calls completed

0 the total number of calls attempted

The reason for monitoring each of these parameters, and the methods of measurement, are described in the following sections

31.3 TRAFFIC INTENSITY

TrafJic intensity, as we learned in Chapter 30, is the measure of the average number of

simultaneous calls in progress on a route between two exchanges, or across an exchange, during a given period of time It is normally measured during the hour of greatest traffic

activity (or so-called busy hour) and is quoted Erlangs There are two principal methods

of measuring traffic intensity: either by frequent sampling of the number of circuits actually in use and calculation of the statistical average, or by using a call logging system which records the start and finish time of each individual call, so allowing the total circuit usage time to be measured accurately Total usage during the period is divided by the duration of the period to give the average number of circuits in use

TRAFFIC INTENSITY 557

A check at least once per month of the busy-hour traffic intensity on each route in a network is essential to make sure there are enough circuits on each individual route Circuit requirements for each route are calculated from these values according to the Erlang formula, using the forecast traffic and the desired grade of service as inputs to the formula

On their sampling days, many network operators are not content to monitor the

traffic intensity during the anticipated busy hour (though this is an accepted practice); they also monitor the traffic activity profile on each route throughout the whole day The profile is invaluable as an indicator of overall calling patterns, revealing short or long periods during the day when the traffic on a particular route is either very heavily congested or is under-utilizing the available capacity In the latter case, the capacity

may be useful as a means of relieving a congested route (by transit routing in the manner discussed in the next chapter) Alternatively, an advertising campaign could be used to stimulate extra network usage

Traffic profiles on different routes vary greatly according to the nature of the route (e.g local, trunk or international) and of its users, and they may change slowly over a period of months Some profiles slowly distort towards a highly peaked, busy-hour oriented pattern, and others reflect a steadier traffic load throughout the day

Highly peaked and busy-hour oriented profiles, of which Figure 3 1.1 is an example, can be problematic for network operators for two reasons First congestion is highly likely during the period of peak traffic demand (even if circuits are relatively well provided), and second over the day as a whole there will be relatively little network use, with correspondingly low revenue

Highly peaked traffic profiles can sometimes be flattened by charging a heavy price premium for calls made at the peak time The idea is to persuade customers to make their calls either a little earlier or later during the day If it can be achieved, a very flat profile such as that shown in Figure 31.2, makes highly efficient use of the network

Charges which depend on the time of day tend to have an effect on domestic call demand, but the effect is less marked on business calls, because few of the callers worry about the cost

Possible Number of

clrcults

m use Number o f

circuits required

Wasted,c.ircuit ovallablltty tlme

1 1 l 1 1 1 1 1 l 1 ~ I

0 2 4 6 8 10 12 2 L 6 8 10 12 Time o f d a y Figure 31.1 Busy hour oriented traffic profile

in use

Number of

circuits required

2 4 6 8 10 12 Higher

Figure 31.2 illustrates an extreme redistribution of the traffic previously shown in Figure 3 1.1 In the course of the day the same number of calls have been completed in Figure 3 1.2 as in Figure 31.1 and thus a similar revenue has been earned, but the flatter traffic profile has meant a lower busy-hour traffic value and a reduced circuit

requirement The profile has been flattened by stimulating ‘time-shift’ of some of the peak traffic, by charging a higher price for the peak period 8 a.m.-2 p.m

There are times when significantly higher busy-hour prices fail to dissuade customers from calling at the busiest time, and none of the traffic is timeshifted When this hap- pens the only way to improve overall network utilization and revenue is by stimulating new off-peak traffic

31.4 TOTAL USAGE MONITORING

Besides coping with its route busy-hour demand, a network must be designed to carry the total volume of demand Exchanges may be limited in their capability to record the details

of individual calls, so that above a certain limit, billing or other call detail information may

be lost In addition, the amount of exchange equipment (switching points, registers, etc.) which is required will depend on the total volume of demand There are two related measures of the total usage of a circuit-switched network or a leased point-to-point

connection The two measures are the paid time and the holding time

The paid time equals the total number of usage minutes for which customers have

been charged In telephone network terms this is the number of conversation minutes

In a telex, or a circuit-switched data network, the paid time is the connected time, the

duration of which has a direct relationship with the total amount of textual information that could be carried

By contrast, the total holding time is the time for which the network itself has been in use, and it is always slightly greater than the paid time, as Figure 3 1.3 shows It includes

not only the ‘connected’ o r ‘conversation’ time, but also the time needed for call set-up

TOTAL USAGE MONITORING 559

Caller handset and dials

lifts Destination Called

telephone party

clears disconnected

1 (Network i d l e )

and cleardown and the ringing time of the destination telephone The holding time is

the better indicator of network resource requirements, but network operators may

choose to monitor either paid or holding time, or both

31.4.1 Paid Time

The paid time is a direct measure of customers’ actual usage This is the time during

which the customer actually communicates, and could be considered to be the best

measure of ‘real’ demand The more paid-time use of a network is made, the greater

is the volume of communication This contrasts with the traffic intensity, which is

only a measure of how many people choose to communicate at the same time It is

paid time that attracts revenue and is therefore the most important financial

parameter The paid time accumulated by particular customers or on particular routes

between individual exchanges gives a strong indication to network operators of where

they should concentrate their resources and efforts, and where they are most a t risk

from competitors Operators can ill-afford to lose valuable customers or the traffic on

profitable routes

There are three methods of either measuring or estimating paid time The first and

most obvious method is to sum the usage made by individual customers However,

although summation gives valuable route-by-route paid minutes statistics when derived

from itemized customer bills, in those networks which only record their customers’

usage as a bulk total number of units on simple cyclic meters (as Chapter 35 discusses),

summation only reveals total network use, without any route-specific paid minute

information However, all is not lost, because the paid time on each route may be

measured directly by sampling the traffic intensity on the route itself at a number of

regular time intervals, and applying one of the following conversion formulae for

estimating the paid minutes

Busy hour paid minutes = Busy hour traffic in Erlangs X 60 X efficiency factor

where efficiency factor = paid time

holding time

(the value is estimated from historical information)

daily paid minutes = 5 or 6 times busy hour paid minutes

(again the value is obtained from historical records)

total monthly paid minutes = approx 22 X daily paid minutes (It is a good enough assumption to estimate an entire month’s traffic as equivalent to that of 22 working days)

31.4.2 Holding Time

In contrast to the paid time, the longer period of holding time represents the total time

when network resources are in use, and the difference between holding and paid time

volumes is the time when the network common equipment (used for call set-up and cleardown) is in use This quantity is called the common equipment holding time The total holding time, the common equipment holding time, and the traffic intensity values all help to determine how many of each of the various equipments must be provided in the network

The amount of a particular type of common equipment (e.g signalling receivers) needed in an exchange is usually calculated by the normal Erlang method, with the common equipment traffic load calculated by multiplying the expected number of calls

by the average common equipment holding time per call Typical grades of service demanded of common equipment might be 0.5% lost calls, 0.1 % or even 0.05%

The total number of calls attempted (also called the number of bids) is the best measure

of unconstrained customer demand, because unlike the paid minute volume or the traffic intensity actually carried by a network, it is not limited by network congestion

At a time of network congestion, the busy hour call attempt ( B H C A ) count may

continue to increase (as unsatisfied customer demand continues to grow) whereas paid minute volumes and traffic intensities may saturate at the maximum capacity of the network A very large number of unsatisfied call attempts is an almost certain sign of congestion, either through underprovision of equipment or resulting from short term network failure, and is a good means of spotting suppressed traffic within a network Unfortunately the exact amount of suppressed traffic is difficult to estimate, because the

persistence of customers in repeating call attempts many times over affects the overall bid count Figure 31.4 illustrates an example of the failure of some call attempts

The number of call attempts can only be measured accurately by monitoring each individual customer’s line A value measured at any point deeper in the network will not

be an accurate measure, as it will have been reduced by the effects of any congestion at the network fringe

NUMBER OF CALLS COMPLETED 561

Small number of attempts lost because no out oing circuits a r e availabi'e,or

i s congested

Attempts lost

In cases where the call attempt count suggests that traffic is being suppressed, then the unexpurgated busy-hour traffic intensity and paid minute demand can be estimated from one of the following conversion formulae

busy hour traffic, in Erlangs =No of call attempts in the busy hour

X (average call holding time)/60 busy hour paid time =No of call attempts in the busy hour

X average paid time per call

Sometimes exchange monitoring limitations make it impossible for network operators

to measure accurately the number of call attempts from a given source to a given destination If so, the the number of call attempts may be estimated as the number of

outgoing circuit seizures As shown in Figure 31.4, however, this estimate will be lower

than the actual because a small number of call attempts (bids) fail in the first exchange

due to congestion, and so do not mature into an outgoing circuit seizure

31.6 NUMBER OF CALLS COMPLETED

The number of calls completed in a network sense (i.e reaching ringing tone or answer),

when compared with the number of calls attempted, gives another measure of the state

of network congestion The proportion of busy hour calls completed, when expressed as

a percentage of the number of calls attempted, should equal the design grade of service Hence

grade of service =

The grade of service is a measure of the frustration that a customer will experience when trying to complete a call during the busiest hour of the day We could calculate the aver- age daily percentage of lost call attempts, but this is not so commonly done, because psychological analysis of customer behaviour suggests that it is of no relevance

(number of busy hour call attempts) - (number of busy hour call completions) X 100%

(number of busy hour call attempts)

The number of calls completed by the network is a difficult quantity to measure, because not all signalling systems indicate the ‘network-completed’ state In the

American network a peg-count overfIow was used to monitor this value but its absence

in other networks means that it is also common to measure only the number and

proportion of answered calls This number is clearly lower than the number

completed by the network as some calls are bound to encounter either a subscriber busy state or a ring tone-no reply condition The proportion of answered to attempted

calls, and that of answered calls to seizures, are known as the answer bid ratio ( A B R ) and the answer seizure ratio ( A S R ) , respectively; they are defined mathe-

matically below Measured over relatively short periods of time (5-15 minutes), both are good indicators of instantaneous network congestion The higher the network

congestion, the lower the ABR or ASR The converse, that the higher the ABR or ASR the greater the congestion, is not necessarily true because calls may remain

unanswered for a range of other reasons (e.g people are simply not answering their

phones) These same uncertainties mean that no conclusion can be drawn from the

actual value of the ABR or ASR A conclusion can only be drawn from the value

relative to its ‘normal’

answer bid ratio (ABR) = no of answers

no of call attempts

answer seizure ratio (ASR) = no of answers

no of seizures

In Chapter 37 the use of ABR and ASR statistics as tools for short term network management surveillance of the network will be described further

31.7.1 Packet-, Frame- and Cell-switched Networks, LANs and MANS

There are two prime factors of importance to data network users These are the overall throughput capacity and the response time (network transaction time or propagation time) of the network

The throughput capacity is the amount of data that can be sent over the network dur- ing a given period Usually a network is designed to cope with the maximum demand of the peak hour, although network cost savings can be realized by queueing up less important data, for transmission outside the peak hour Such queueing leads to more effective 24-hour use of the network

Throughput capacity can be measured in bits per second (bit/s), messages per second, packets (frames or cells) per second, or segments per hour (1 segment = 512 bits)

In designing and upgrading network capacity, it is important to consider the practical net- work throughput rather than just the transmission line speed capacity The practical network throughput will always be less than the line speed capacity, first because not all messages are received without errors and some have to be re-transmitted, and second

MONITORING USAGE OF DATA NETWORKS 563

because some of the available line capacity is effectively ‘wasted’ in gaps between messages The two effects are summarized in the formulae below

successful message throughput ( W ) = correct messages received

time duration taken writing it another way

time taken for message transmission + wasted time between messages

where M = average message length in bits; P = probability of errors in the received message; R = line speed in bits per second; T = average line time ‘wasted’ between

messages; M / R = time required to transmit average message

Rearranging the formulae, so that we can relate the required network line speed to the required user data throughput

(1 + T R / M )

l - P

R = TP

This formula ensures an adequate average throughput capacity of the network, but another equally important characteristic of a data network is the response time The computer systems linked by data networks will have been designed to carry out a given set of functions within a given period of time, to maintain for example a database of share prices updated at least once every hour, or (more onerously in terms of response time) to control the trains on a metropolitan underground railway

The overall response time of a computer and data network includes the time taken to transmit an error-free message along the line and back, plus the computer processing time at the far end of the communication link, and the line turn-around time (if the link

is half-duplex) The line transmission time includes not only the period during which the

line is conveying data, but also any time spent waiting for any previous messages to be

transmitted or acknowledged If the response time of the network is too long, then the

network designer must increase the network throughput capacity, which he can do either by upgrading line speeds (as might be appropriate on a LAN or on a point-to-

point leased circuit network, using say 9600 bit/s modems rather than 4800 bit/s ones)

or by providing a larger number of circuit connections (as might be appropriate in a circuit- or packet-switched data network)

The dimensioning method of calculating what overall bit throughput is required to meet a given response time constraint is based upon the Erlang waiting time formula

described in Chapter 30 As might be expected, the faster the required response time,

the greater is the transmission linespeed required (see also Chapter 20)

In conclusion, no matter which dimensioning method is used, if the network is to have adequate capacity to meet the user’s throughput demand, the transmission line speed must be chosen with a relatively higher capacity, sufficient to counteract the effect

of errors and the ‘wasted’ time between messages The ‘wasted’ time, incidentally,

includes not only periods of instantaneous non-use, but also includes some of the data overheads which accompany each message (e.g the header and destination codes of the

network protocol about which we learned in Chapter 9)

Because they have a marked impact on the performance of data networks, it is worth digressing for a moment to discuss the effect of data packet, block or frame lengths As

we learned in Chapter 9, the protocol used to convey data messages breaks these

messages down into a number of frames, blocks or packets, and transmits each with some other overhead information which is needed to ensure delivery to the correct

destination and to control the frequency of errors During periods of line noise

disturbance, longer packets of data are more likely to be affected by the noise than shorter ones The result is a lower effective data throughput and a slower response time because of the extra workload imposed by the need for large scale data re-transmission

On the other hand, shorter blocks have the disadvantage of decreasing the throughput

at all times (whether the network is busy or not) This is because of the overhead of

protocol headers, one each for the larger number of packets or frames

31.8 FORECASTING MODELS FOR PREDICTING

FUTURE NETWORK USE

The operation of networks, their efficient utilization, carriage of traffic, and their suc- cessful evolution all depend critically on accurate estimation of future needs It is vital for network operators to have an efficient method of forecasting the traffic the network must carry so that a network of sufficient size can be maintained to meet users’ needs Forecasts need to cover both short and long-term periods so that the whole business

of providing for the future, the planning and the capital investment, can be put in hand

in good time For straight circuit provision a twelve month forecast may be adequate, but for normal extensions of major switching and transmission equipment it is necessary to look 1-5 years into the future, in line with the ordering lead time Even longer forecasts may be needed when planning new cables, laying new ducts, and setting

up major new sites The longer term forecasts are inevitably less accurate than the

shorter, but because there is time for adjustment, this is acceptable

Forecasting uses historic measurements of network usage, and particularly of growth

in usage, to predict likely future customer demand A number of different forecasting

models have been developed, but none of the methods are 100% reliable This is hardly surprising as they all involve attempts at predicting future human behaviour The reli- ability of any forecast relies on the accuracy of the historical information and the period over which it has been collected The shorter the period of historical knowledge and the lower the number of observations on which it is based, the less reliable is the forecast

In general, the selection of an appropriate forecasting method ensures that the forecast is accurate for a period into the future approximately equal to one-third of the period of the historical information Thus a one year forward forecast should be based on at least three years of historical information The best way to select a particular forecasting met- hod for use in any given circumstance is by experience If the model appears to give reliable results in practice and therefore is a ‘good predictor’, then use it, if not; try another method