Estimating network communication speeds for

office automation

John McCallum, K Ranai and B Srinivasan* show how to estimate the network speed required for different automated office sizes

Communication networks designed for offices need to handle a variety of text, voice, data and images. Such office communication systems should not impose extraneous limitations on the work done by the office workers. Applying simple measures of human factors results in predictions on the requirements and the nature of future office communications systems. The average information communication rates are dominated by voice. Peak data rates are generated by transmitting images due to human factor considerations. Peak text rates approach voice data rates. Data compression techniques and high performance image compression processors are likely to be necessary to meet communication requirements of the office. Communication network performance will have to be increased significantly from current network speeds to prevent information bottlenecks.

Keywords: computer networks, network speed require- ments, office networks, multi-media communications, network modelling

There has been considerable interest in the use of local area networks (LAN) to provide the communications backbone for offices. Early studies of office automation 1 were often centred on the technology of LANs. Recently, several researchers have looked at how time constrained information can be used over a network 2. Most of the interest has centred on how voice can be mixed with

Department of Information Systems and Computer Science, National University of Singapore, Singapore 0511 *Department of Computer Science, Monash University, Australia 3168.

0140-3664/90/020099-09 $03.00 ©

vol 13 no 2 march 1990

other data over a packet switched network 3-8. However, the general office requirements for network performance have not been considered in detail.

The goal of our research is to find the approximate speed requirements for a LAN capable of handling the communications needs for an office. The optimal speed of the network is dependent on many factors, such as the size Of the offices, the quality of the communications, and whether data compression is used to reduce the data transfer rate.

For an individual, the base or average communication rate can be determined from how much information is handled by an individual in an office in an average day, and the form of representation of the data. The individual peak data rates are determined by human factors which determine the minimum acceptable delay times, mainly for voice and for image transmissions.

The network requirements are dependent on the average and peak communication rates for individuals, as well as the number of concurrent users of the network. The efficiency of the network and the tolerance for allowing less than ideal communication performance are other major factors in determining the network speed.

The efficiency of a network is determined by several factors, such as the error rates, the number of connections to the network, the overhead in packetization, the speed of the adapters, as well as the type of network being used.

OFFICE C H A R A C T E R I S T I C S

There are two general categories of office in an organiza- tion: the procedural office (also called the Type I office 9)

1990 Butterworth & Co (Publishers) Ltd

99

performs routine organizational operations; and the dynamic office (or Type II office) handles unstructured i n fo rma t ion - the management office. Early data pro- cessing business automation concentrated on the pro- cedural office. Office automation is more concerned with the dynamic office. In real situations, however, the distinction between the office types is not so easy to define. Even high level executive offices have some routine job duties, and employees in a fully automated procedural office need to handle non-standard situations in exceptional ways.

Office automation is the use of technology to improve the productivity and effectiveness of administrative and other assorted tasks in offices. Office automation is directed towards the unstructured aspects of the organ- ization as compared with data processing, which has concentrated on the formal business operation of the organization. Initially, office automation was directed primarily towards the lower level people in the organiza- tion performing basic business operating procedures. However, higher level administrative functions of manage- ment are being affected as the capabilities and usefulness of commercial office systems develop.

In one of the recent studies I 0, it was estimated that the operational information of the organization (procedural offices) represents only a minor part of the information " flow of the organization. The office automation functions, or the dynamic office functions of the organization, are the main users of communications in the organization.

The activities that are generated in an office are of interest in determining the quantities of information that are handled (transmitted to and processed) daily by individuals, and in determining the time periods over which the communication activities take place.

The time usage by different categories of workers in offices has been studied by several investigators 11-13. Obviously, the studies did not produce identical results, because offices vary significantly. However, patterns do emerge. Table I shows the time usage breakdown of different categories of office workers. More detailed breakdowns of time usage are given in the original studies.

Table 1. Distribution of average time use by managers (M), professionals (P), technicians (T), secretaries (S), clerks (C) and for all office workers (A) (after McCallum and Yap 13)

% of time by activity

M P T S C A

Interpersonal communication 34.2 26.7 20.8 17.5 13.5 24.3 Document creation 13.0 18.6 7.2 7.3 16.2 13.4 Information analysis 28.4 28.2 34.9 23.8 27.3 27.6 Using equipment 2.3 10.9 18.6 36.2 19.3 14.7 Less productive time 20.4 11.2 18.6 12.4 19.5 17.O Other 1.8 4.4 0.0 2.9 4.3 3.0

Total 1 OO.0 1 OO.0 100.0 100.0 1 OO.0 100.0

The time distribution in Table I shows that communi- cations tasks are not the only tasks performed. The category of interpersonal communications includes about 10% of time for telephone usage, but it does not showwhen the communication activities are performed.

Normally, communication traffic rates are based on peak hour rates. It is well known that low traffic periods occur during the early working hours, the lunch period and just before quitting time. A good approximation to this activity is to say that the activity is uniformly distributed over a five hour period (using the following periods as a basis: 10.00 am to noon; and 1.30 pm to 4.30 pm).

The form of unstructured information that passes through the office has also been studied. Table 2 gives the average paperwork that passes through a typical office desk, and Table 3 gives quantities of information which are associated with different types of documents. The quantities of information, when expressed in compressed form are also given in Table 3.

Table 2. Mean number of pages of information across an office worker's desk per year (250 working days). Publications include newspapers, books, magazines and catalogues. Documents include general types of documents, including computer forms. Messages include letters, telex and facsimile messages. Financial forms include bills, receipts, invoices and similar financial documents (after McCallum and Yap 13)

Information No No Average type pages~day items~day pages/

document

Publications 151 5.0 30.2 Documents 105 17.0 6.2 Messages 52 22.3 2.3 Financial forms 31 22.5 1.4 Total 339 66.8 5.1

Table 3. Typical quantities of information in documents. The measured quantities 14 have been converted to equivalent measures in bits based on the coding methods. The compressed information quantities are approximations, based on the discussion in the text

Item Information in documents

Measured (in bits) quantity Basic Compressed

Preprinted form 200 chars 1600 2 l° Typed page 2479 chars 19832 213 Small black and

white photo 48 c m 2 480000 216 Large colour photo 139 c m 2 5560000 218

Diagram (plot) 100 points 3000 211

I00 computer communications

INFORMATION QUANTITY AND COMPRESSION

The approximate information content of different types of document can be estimated, a summary of which is given in Table 3. The values in Table 3 have been estimated using the following considerations: a normal page contains about 2 479 characters; a typical preprinted form only has a fraction of the space to fill in, of about 30 words (or 200 characters) per form. There are six characters per word, and eight bits are normally used to represent each character for storage and manipulation in computer systems.

Line drawings can be represented as a set of drawing primitives, such as lines, arcs and filled patterns. Assuming that a small diagram can be made up from about 50 lines, which connect I00 points, the diagram can be represen- ted by about 2 500 bytes. Each line consists of a byte for each x and y coordinate, for each point, and a byte to specify that a line should be drawn, rather than an arc or other drawing primitive.

The photographs contain a significant amount of information 14. A typical black and white photograph has a

2 2 size of about 48 cm, or about 2.5 inches. Colour photographs are about 139 cm 2 (4.5 inches2). Black and white photographs have a resolution of about 90 lines to the inch, with about 256 grey levels per pixel. Black and white image resolution corresponds to about I0 000 bit/ cm 2. A colour photograph is printed in four colours (cyan, magenta, yellow and black) with a resolution of about 90 lines/inch. Each colour of the pixel can have about 256 values. A colour photograph has a resolution of about 40 000 bit/cm 2.

The data compression ratios (the ratio of the uncom- pressed data to the compressed data) depend on the form of the information, and on the data compression method used (see Lynch 15 for an overview of data compression methods). The compression ratio for text is typically 16 in the range from 1.7 to about 3. Data has a similar compression ratio (of about 2) to that of general text 17. Data compression is also applicable to preprinted forms and to line diagrams.

Image compression is dependant on the type of

images. Facsimile transmission of standard documents achieve compression ratios TM of about 9.5 for group 3 facsimile (using a resolution of 200 X 100 lines/inch), and about 28 for group 4 facsimile (using a resolution of 400 x 400 lines/inch). However, the test documents are mainly text, which is not representative of general photographic images. In fact, facsimile usually expands the amount of information contained in a text document, by treating simple text as graphic information instead of ASCII codes.

Basic! photographic images can be compressed by about a factor of eight, to a representation of about 1 bit/ pixe119. Colour photographs can be compressed to about 2 bit/pixel (1/2 bit per colour).

Full motion television images can be compressed 2° by a factor of about 1 440, but a significant amount of the compression is due to the time invariance of the image. Full motion images have not been included in this study because there is no current office use of full motion imagery. However, full motion imagery could become important in future offices using video telephones or more widespread teleconferencing facilities.

An estimate of the information flow across an average office desk can be obtained from Tables 2 and 3. Financial forms were assumed to consist of standard forms; messages and documents were assumed to consist of standard typed pages; an average publication page is assumed to consist of standard text (2 479 characters or equivalents) plus 6.23 cm 2 of black and white image, and 0.4 cm 2 of colour image per page. Various compression techniques can be used to reduce the amount of information. The resulting information quantities are listed in Table 4.

The information content of voice signals (telephone)is often defined in terms of its simplex data rate. The simplex values should be doubled for full duplex voice communications. The telephone system uses a bandpass of about 3 500 Hz. Digital telephones transmit simplex data at 64kbit/s21; standard duplex circuits require 128 kbit/s; vocoders can compress the data rate to approximately 2 400 bit/s 23. Using more intense pro- cessing, this rate can be reduced to 800 bit/s. However,

Table 4. Quantity of information in different forms through an office. Values are calculated from the data presented in Tables 2 and 3

Information quantities (in bits)

Info/page Info/doc

Pages~day B a s i c Compressed Documents~day Basic Compressed

Publication 151 98123 18705 5.0 221 219 Document 105 19832 9917 17.0 217 2 TM

Message 52 19832 9917 22.3 215 214 Financial form 31 1600 800 22.5 211 21° Total or average 339 53038 12998 66.8 2 TM 216

Total information flow per office per day 339 pages 66.8 documents

5.1 pages per document 18 Megabits basic (224 bits)

4.4 Megabits compressed (222 bits)

vol 13 no 2 march 1990 101

the quality of speech reproduction deteriorates at low data rates.

Smith and Benjamin 1° use a typical voice communica- tion rate of 5.2 hundred call seconds (ccs) for peak hour utilization, with one third of the traffic off-site. If the peak hour rate is assumed to be constant over a five hour period, and negligible calls are made over the rest of the work day, this 5.2 ccs corresponds to about 43 min of actual telephone conversations (based on the peak rate for a five hour period). This rate is comparable with most time usage surveys, which show that the time spent on the telephone is about 10% of the working day (10% of eight hours is about 2 880 seconds, or 48 minutes). One person generates about 330 Mbits of voice traffic per working day when using digital, full duplex communication channels, or about 6 Mbits in compressed speech form. The printed information equivalent to what is said is approximately 0.25 Mbits. These numbers indicate that the information bottlenecks occur predominantly in voice communication, unless voice is reduced to its printed equivalent.

H U M A N FACTORS A N D DATA TRANSFER RATES

In attempting to model the information transfer process, we need to decide what elements should be studied: the bits, the bytes, the pages orthe documents. People use or review information in chunks, normally being physical divisions of information such as pages or screens consisting of blocks of text, or images, or a mix of both. For this reason, pages are our choice for the basic information element.

The instantaneous rate for communicating a page of information is an important human factor consideration. When reading or browsing through documents, the speed of transmitting the next page becomes important. It is generally accepted that 0.25 s is an effectively instan- taneous response for browsing (see Reference 24 for a good review of response times). Lundy 2s presents a diagram showing that the productivity of workers is improved with fast response times, limited by about 0.25 s. Transmitting one page of a text document (2 479 characters) within this 0.25 s requires an information transmission rate of (2479 X 8/0.25) approximately 80 kbit/s.

Voice does not exist in pages - voice is created in talkspurts, which collectively form either a voice annota- tion or a conversation. An average talkspurt has been found to be about 1.34 s followed by a silent period of about 1.67 S 26. We call the talkspurt and silence pair a voice page, or a logical voice segment. Voice conversa- tions are time critical. The voice pages must be sent within a short time period, typically with an end-to-end delay of about 0.25 s, similar to the delay for displaying visual pages. The maximum delay implies that the voice pages must actually be sent as multiple packets of data.

The peak information flow based on a 0.25 s response time occurs for uncompressed average sized colour images which would require a communication rate of 22 Mbit/s (1.4 Mbit/s compressed). A summary of the weighted average of voice, data and textual information communication rates is given in Table 5.

The peak communication rates listed in Table 5 are based on a single individual in the office. The peak communication rate for a network connecting all people working in the organization's offices is the peak rate for

Table 5. Peak information communication rates in the office measured in Mbit/s for different forms of information. The peak rate is the instantaneous speed required to transmit (one page of) the information within the time indicated by human factors. Colour photographs require the highest peak communication rate. A voice page is the content for an average utterance. The information contents of the different pages are taken from Tables 3 and 4

Form Basic content Peak communication (Mbit/s) rates (Mbit/s)

Basic Compressed

Colour photo 5.560 22.0 1.39 * Black and white photo 0.480 1.9 0.24

Publication page 0.098 0.39 0.07

Average visual page 0.053 0.21 0.05

Text page 0.020 0.08 0.04 Voice 0.128 0.128 0.0024 Voice page 1.6 1.6 0.03

the individual times the number of people in the offices, assuming that the overhead involved in the network transmission is negligible to the amount of data trans- mitted. The number of office workers varies greatly in different organizations, and needs to be treated as a variable in determining communication rates.

It is useful to have a base number that can be used as a typical number of office workers. Smith and Benjamin 1° reported office sizes for 24 geographical locations of a particular Xerox organization. Using their figures, the office sizes range from 20 to over 500 office workers. Using their average figures for non-service people, the average number of office workers at a geographical site is about 115. We also estimated the average number of office workers in an organization to be 115. This number was estimated on the basis of the distribution of the sizes of manufacturing industries in the UK 27, and by about 30% of the employees being office workers.

The smeared daily communication rates (defined at the peak hour) are determined by assuming a uniform transmission rate of all of the data over a five hour (18 000 s) period. The smeared network communication rate is the individual rate times the number of office workers. The smeared communication rate is actually the minimum rate assuming that no human factors are important. The smeared rate is unrealistic for small networks, since it is obvious that human factors such as the voice response time in conversations affect how people use the communication facilities provided by the network. In very large networks (as the number of users approaches infinity) with constant usage rates, however, the smeared rate gives the minimum lower bound to the network speed. Table 6 contains a summary of the smeared individual data transmission rates.

APPROXIMATE NETWORK PERFORMANCE MODEL

Several media have been suggested for supporting office communications. The two major competing technologies

102 computer communications

Table 6. Individual smeared data rates (K/twd) required to handle visual (paper equivalent) and voice communications in an office. The smeared data rate is the minimum throughput required for transmitting all of the data at a uniform rate throughout the effective working day. The smeared rate does not incorporate human factors and is impractical other than as a minimum lower bound on ideal very large networks

Data transmitted daily (Mbit/day)

Smeared data rates (kbit/s)

Basic Compressed (Pages/ Basic Compressed day)

Visual 18 4.4 339 1 .24 Voice 330 6.2 828 18 .34 Total 348 10.6 1167 19 .59

are LANs, and the combined voice and data private automatic branch exchange (PABX) based on integrated services digital network (ISDN). This paper is concerned with network-based communications, since the basic ISDN data rate cannot handle the peak rates required for image transmission for an individual, even when the image data is compressed.

A LAN can be modelled as a single server queue 28 where the wire acts as the server, since only one message can be transmitted through the wire at any time. Each user of the network has to wait for traffic in the network to finish before it can transmit across the network. The time that is required to send a message is the sum of the waiting time, and the time to transmit the message between stations on the network.

A queueing model has four major parameters of interest in our model: the arrival rate (Z), the mean service rate (u), the traffic intensity (p), and the time spent in the system (Ts).

The mean arrival rate (Z) of pages is determined from the average number of pages sent and received per day per person, and from the number of people sending and receiving information. A message sent from one person to another in the same office (on the same network) can only be counted as a single message for the network load. The percentage of local communications (~) must be discounted. Normally, about 80% of the communications occur locally within the organization, and 20% go to people outside. The mean arrival rate of pages is:

~. = hN(1 - ~/2)/twd (1)

where h is the number of pages sent or received per day per person, N is the number of people connected to the network, g is the percentage of local traffic, and twd is the length of an effective day (18 000 s). The effective day is chosen to be five hours (18 000 s) to make the rates applicable to an average peak hour rate. The number of information workers depends on the size of the office.

In office work, the collection and distribution of information is basically a random task. A first approximation to the typical distribution of the arrival of pages is a Poisson distribution. The random arrival of pages corres- ponds to an interarrival time represented by an exponen- tial probability distribution function. A better approxima-

tion is not possible realistically, since measurements of office activities have not characterized the distribution of events in offices to a significant extent, and office activities are changing with time.

The mean service rate (/1) of pages is given by:

tl = rlEhlK (2)

where q is the rated network speed in bits/s, E is the percent of network efficiency, h is the number of pages sent or received per day per person, and K is the total daily information sent or received per person. The mean service time for a page is 1//~ s.

The effective network speed (r/E) is the network speed after the inefficiencies of the network have been taken into account. The practical limits of throughput in a packet switched network depend on many factors, such as the packet overhead, the error rate, the access mechanism, contention due to packet collisions, the packet size, the physical length of the wiring, the packet distribution time, the network utilization, the performance of the interface adapters and processors, and the software efficiency. The effective network speed 29 is very much lower than the 90% to 100% efficiency of the wire connecting the stations. A maximum practical limit appears to be about E = 25% based on laboratory throughput measurements 3°. Generally, the maximum average throughput rates on PC-based Ethernet networks are in the order of 600 kbit/s, or about 6% of the rated Ethernet network speed. Better software and adapter cards should enable the effective network speed to be raised to about 25% of the rated network speed. In our calculations, a constant efficiency was assumed, since we are less concerned with the type of network than the required speed of the network.

The distribution of service rate about the mean service rate ~) is attributable to two major factors: the size distribution of the pages to be transmitted, and the non- linear loading effects as the traffic density increases. The size of the pages is the major determinant of the service rate. The standard deviation of page size is approximately equal to the mean page size for mixed text and image pages TM. Therefore, we set the standard deviation of the page sizes to the mean page size, and the variance of the page service rate O'p 2 to the square of the mean service rate.

The traffic intensity (p) in Erlangs is given by:

p = Z/# (3)

If the traffic intensity exceeds one, then the network will obviously be overloaded, and the messages will back up. Therefore, the value of the traffic must be restricted to less than one. If the traffic were greater than one, then multiple networks or isolated segments would be required to handle the load.

We are interested in finding the speed of a network for which the time that any message spends in the system is less than d t = 0.25 S, the time considered excessive for human factor considerations. The expected time spent in the system for an (M/G/ l ) queue is given 31 by:

Ts = (p2 + ; t2~ 2)/(2~(1 _ p)) + I /U (4)

This can be recast into another form:

T s = (1 + p/2~2c~ 2 - 1))/~(1 - p)) (5)

vol 13 no 2 march 1990 103

If the variance of the page service time distribution o 2 is equal to the square of the mean service time (1//22), the equation can be seen to be the same as that for T s for the simpler (M/M/ l ) queue:

T s = 1/(/2 - A) = 1/(/2(1 - p)) (6)

In an (M/M/ l ) queue, the variance of the time spent in the system is the square of the time in the system.

Different types of networks are best represented by different queueing models. The average time of a page spent in the system for a general network can be seen from Equation (4) tO be linearly dependent on a~ 2, the variance of the page service time distribution. In a fixed slot deterministic network model (M/D/ l ) , a~ is zero. In a non-deterministic network, such as a heavily loaded Ethernet network, it could be very large.

For a general network, a rough first approximation is given by a (M/M/ l ) queueing model. This approximation may not be good for a typical network, especially when working with a non-deterministic network such as Ethernet. However,, the (M/M/ l ) queueing model was chosen because we felt that the simple expressions derived from the rough approximation gave a better qualitative understanding of the factors controlling the network performance than a more accurate quantitative model based on the (M/G/ l ) queueing model.

Based on the choice of the (M/M/ l ) queueing model, the standard deviation of the time spent in the system (~ts) is simply the same as the time spent in the system. To ensure that messages are sent through the network within the d t = 0.25 s, then we should look at the mean service time plus two standard deviations, and compare that time with dt.

d t = T s + 2O'ts

= 3/(p(1 - p)) (7)

= 3/(/2(1 - A//2))

o r

/2 = 3/dt + Z (8)

The rated or nominal network speed necessary to handle the daily traffic through the office with the time constraints for human factors can be determined as:

T1 = 3K/(dthE) + (K/twd)N(1 - ~/2)/E (9)

where 7/is the rated network speed in bits/s, K is the daily information sent or received per person, d t is the maximum time in seconds to transmit a page, h is the number of pages sent or received per day per person, E is the efficiency of the network, N is the number of office workers on the network, D, is the percentage of local communications, and twd is the length of an effective working day in seconds.

Equation (9) has been evaluated for a variety of values of N to determine what network speed is required for different circumstances. These values are given in Table 7 for compressed and uncompressed forms of information, and considering the inclusion and exclusion of voice from an office network. The values in Table 7 have been plotted in Figure 1.

Equation (9) has two components which have very different properties. The first component is independent of the number of users of the network. The component is dependent on how fast (dr) a typical page of information

Table 7. Network speed in Mbit/s required for different numbers of office workers. Four types of network are considered: with and without voice data for both uncompressed and compressed representa- tions of the data

No. of Compressed data Uncompressed data workers (N) With Without With Without

voice voice voice voice

1 0.4 0.6 14.5 2.5 2 0.4 0.6 14.5 2.6 4 0.4 0.6 14.6 2.6 8 0.5 0.6 14.8 2.6

16 0.5 0.6 15.2 2.6 32 0.5 0.6 15.9 2.6 64 0.5 0.7 17.4 2.7

128 0.6 0.7 20.4 2.9 256 0.8 0.8 26.4 3.2 512 1.1 0.9 38.4 3.8

1024 1.9 1.2 62.3 5.0 2048 3.4 1.8 110.2 7.5 4096 6.3 3.0 206.0 12.4 8192 12.1 5.4 397.6 22.2

16384 23.8 10.2 780.7 41.8

~t

I ooo ,j+/÷/ IOO

~ + I + /

m

IT 2048 8192_ O, I I I I I I I I I I I I I

I 2 4 8 16 32 6 4 128 256 512 1024 4 0 9 6 16384 NO. of people on network

Figure 1. The number of people using a network increases the rated network speed required to properly serve the number of users. The required network speed also depends on what type of information is transmitted through the network. The graphs show the required network speeds for: e: combined visual (text and images) and voice data represented using data compression techniques; + : combined visual and voice data represen- ted in uncompressed form; O: visual data only represented using data compression techniques; rl: visual data represented in uncompressed form

(K/h) can be sent, and ensuring that there is sufficient excess capacity in the network (the factor of three). This response time component dominates the network speed calculation for a small number of people using the network.

The second component used to determine the network speed is based on the minimum network speed required to transmit all of the information (K) sent or received by all of the workers (N(1 - ~,/2)) during the period of the day (twd). The controlling factor is the smeared data rate, as shown in Table 6. The human factor characteristics of the pages sent have no effect on this value. This lower bound component dominates the

104 computer communications

1000

"~ / + / ~ bound I00 Minimum for c010ur photograph

~linimum for overoge page size + t " /

,~_÷__~+.---I -----+'~ / I , ~ , ~ , , v / I , 20~8 ~ 81,92

I 2 4 8 16 32 64 128 256 512 1024 4096 16384

No. of people on network

Figure 2. The network speed required to serve different numbers of office workers effectively has two major factors: the minimum speed required to transmit an average page of information (minimum for average page size), and the total throughput that the network must sustain (the lower bound). These correspond to the two components of Equation (9). The minimum required network speed to serve a typical number of people in an office is determined by the amount of data in one colour photograph transmitted within 0.25 s. The curve plotted here is calculated from equation (9) for mixed visual and voice data represented in uncompressed form

network speed calculation when a large number of users are connected.

The response time component is very sensitive to the average page size and to the maximum delay time allowed. The response time component can vary con- siderably, since the page size for voice is an arbitrary choice, and the average optical page size does not represent a typical page of colour photography. For this reason, it is more useful to choose an arbitrary value for this component, which will meet the performance requirement for transmitting a large colour photograph. Figure 2 shows the nominal network speed versus the number of people using the network for uncompressed voice and visual communication. Figure 2 shows more detail than Figure 1 about the choice of the response time and lower bound components.

A simple conclusion from examining the graphs in Figure 1 is that the most efficient network should serve a very large number of users, and it should operate in the range of about 100 to 300 Mbit/s. A second conclusion is that for small networks, the minimum network speed depends on what is transmitted over the network, rather than on how many users are on the network.

One minor problem in trying to serve a large number of users on a single network is that broadcast nets are limited to about 200 users at maximum on a single segment, due

32 to analog signal processing requirements . However, repeaters and gateways can be used to expand the networks.

DISCUSSION

The required network speed has two controlling com- ponents: one based on human factors considerations and peak communication rates; and another due to the total communications throughput.

In small networks, the required network speed is controlled by the characteristics of the information that is

transmitted within a limited time for human factors reasons. The speed of a small network is relatively independent of the number of users.

In very large networks, the required network speed is controlled by the total amount of information through the network, with little regard for the characteristics of the information.

Office networks are generally small networks within these definitions, and the network speed will be deter- mined from the characteristics of the pages of information, rather than the bulk, or average communications.

In an office communication network, the major average communications load is due to voice traffic. Data due to text transmission is a minor component. However, high resolution colour imagery, such as the photographs in advertising brochures, contains the largest amount of information that is transmitted within a limited time.

The human factor consideration that a photograph should be transmitted instantaneously (or effectively within 0.25 s) determines the peak communications rate ~v.hich is required through the network. This speed is the minimum acceptable effective network speed. Colour photographs make up a small percentage of the total communications in offices. However, they set the minimum network speed.

Our calculations indicate that a minimum office network should have a nominal speed of about I O0 Mbit/s to be able to transmit colour photographs without data compression within 0.25 s. In practical terms, this speed allows the browsing of an electronic equivalent of a colour catalogue from an office terminal. However, a network that has the speed to handle convenient colour image browsing should also be capable of handling all of the daily communications for about another I 000 workers without increasing the bandwidth of the network.

There are some limitations of the model that we have developed here. To build a high accurate model of an office communication network, the following factors need to be considered.

We have assumed that the network efficiency E is constant at about 25%. This assumption is a rough first approximation. However, building a more accurate model of network performance requires more study of the efficiency of specific: networks. An accurate model also requires more detailed statistics about the nature of the information to be communicated, and more detailed modelling of the transmission requirements of each of the types of information.

Our calculations assume that the communications are from person to person (one person is receiving what another person is transmitting). It assumes that store and forward servers are not used (a person transmits to a machine that stores the message, then later the machine transmits the message to the second person, requiring two transmissions and two receptions over the network). The addition of on-line filing systems for text increases the volume of traffic over the network. Basically, it removes the factor (1 - 1"),/2) in the equations. Voice conversations are direct communications, rather than store and forward. However, since full duplex channels have been used in our calculations, the effect of store and forward messages cancels out. The effect of store and forward, or on-line filing systems, adds significant load to the network if browsing of colour photographs becomes common.

The time required to compress and expand data has been assumed to be zero. As well, the communication of

vol 13 no 2 march 1990 105

the message does not necessarily mean that the message gets onto the screen at the same time. The time delays imposed by processing of the signals for expansion, compression and reconstruction onto a screen or sound transducer must be considered for a highly accurate model.

A simulation model of the network would be better able to model the effect of different types of information page sizes and network packet sizes for the different types of information. Also, daily work habits could be model led more accurately than just assuming an effective five hour busy period.

Our model provides a first approximation of the required network speed for different sizes of offices, based on measured information f low rates. More detailed models can be built using this as a basis.

ACKNOWLEDGEMENTS

This research was supported in part by a research grant (RP 41/82) from the National University of Singapore.

John C McCallum is a Senior Lecturer in the Department of Information Systems and Computer Science at the National University of Singapore. He has been teaching a course on technical aspects of office automation for several years. He obtained a PhD in Experimental Space Science from York University in Toronto, Canada. His interests include high-performance computer architecture, benchmarking, system

modelling, organizational modelling, and information flows in organizations.

B. Srinivasan holds a PhD in Computer Science and is presently working as a Senior Lecturer in the Department of Computer Science, Monash University, Melbourne, Australia. His main areas of research include databases, user interface management systems and office automation. He has published many papers relating to the development of algorithms for databases (both centralized and distributed) and

their implementations. Recent work includes the performance evaluation of physical organizations and enforcement mechanisms for integrity constraints within databases. Dr Srinivasan is a member of the Association for Computing Machinery.

K. Ranai has been Lecturer in the Department of Information Systems and Computer Science, National University of Singapore since 1985. He received his BSc(Hons) in Computer Science from the University of Liverpool, England in 1979 and his PhD from the same University in 1984. His research interests include data communica- tions, local-area networks, software complexity metrics, and computer

system performance evaluation and modelling. He is a member of the British Computer Society and the IEEE.

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