UNIVERSITI PUTRA MALAYSIA

TRAFFIC CONTROL IN A SYNCHRONOUS TRANSFER MODE NETWORKS

IBRAHIM KHALIL

FK 1995 2

TRAFFIC CONTROL IN ASYNCI-IRONOUS TRANSFER MODE NETWORKS

By

IBRAHIM KHALIL

Thesis Submitted in Fulfillment of the Requirements for

the Degree of Master of Science in the Faculty of Engineering

Universiti Pertanian Malaysia

Septem ber 1995

ACKNOWLEDGEMENTS

First and foremost, I would l ike to express my earnest gratitude and heart-felt

thanks to the chairman of my supervisory committee, Associate Professor Dr.

Borhanuddin Mohd Ali, for his guidance and support throughout my research. After

drafting me into the MS programme, he helped me find prestigious graduate assistantship

for more than a year. He introduced me to the field of high speed networks and l aid the

foundations of ATM network, upon which this thesis is based. Time and again, his

insightful comments helped me to refine ideas, and his impromptu suggestions opened new

and exciting avenues for further research. Dr. Borhan was encouraging and sympathetic

even during times when I was confused and unsure about my research.

It is also a great honour and pleasure to acknowledge Dr. Abdul Rahman Bidin,

another member of the supervisory committee, for his technical support, helpful

suggestions and insights. Or. Abdul Rahman suggested that I apply the concepts of Neural

networks to ATM call admission control , which led my research effort in this area. His

easy going manner and unflagging team spirit made him a pleasure to work with.

Most of conference and journal articles relat ing to the research reported in this

dissertation carry a third or fourth name on the byl ine. I was really lucky to have Dr.

Malay Raj Mukerjee, an associate professor at UPM, as a collaborator, and sometimes as

a criticizer. His helpful comments on bandwidth allocation was simply great.

J had help from many other people over internet . Those making helpful comments

are l iterally too numerous to mention. However, I would l ike to acknowledge particularly

the advice and support of Dr. D. C. Verma, formerly a Univ. of California student and

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now working at IBM. His Ph.D. dissertation, traffic generator and regular critical

comments through e-mails helped me a lot to improve my work on characterizing

variation of traffic parameters. Thanks also go to Ramesh Nagaraj an, for his Ph.D.

dissertation and explanation on Quality-Of-Service� Hiroshi Esaki for papers on Call

Admission Control (CAC) and pointing out to me the real research issues on thi$ subject�

1. Neves, Hiromatsu, Nordstorm for sending their works on CAC using neural network. I

received a few other research related Ph.D. dissertations from Andreas Pitsilledis of

Swinburne Univ. of Tech. , Paul Skelly of Columbia university, Hui Zhang of University of

California at Berkeley. All these greatly influenced my research work.

Thanks are also due to many individuals who helped me In many ways. I am

grateful to Prof Tatsuya Suda and Prof Aurel Lazar for their generous help while they

visited UPM during my early period of research. Prof Fred Halsall spent considerable

amount of time advising me on the organization of the dissertation.

Last but not least, I must mention the tremendous support I received from my

parents. I would not have finished this thesis without their support.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEME NTS .......... .................................... ............................ ..... 111

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX

LIST OF FIGU RES. . . . . . . . . . . ................ ..... .......................................... ................ X

LIST OF ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

ABSTRACT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ................................................. xv

ABSTRAK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVl1l

CHAPTER

I INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

A TM Network Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Statistical Multiplexing in ATM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . .. . 7 New Characteristics of High Speed Networks. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 9

Research Contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

II REVIEW ON TRAFFIC CONTROL ME THODS IN ATM N ETWOR KS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Virtual Path Band wid th Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Flexibilities in VP Band wid th Control. . . . . . . . . . . . . . . . . . . .. . . . .. .. . . . .. . . . .. . . 17 VP Capacity Reservation and Allocation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 18

Call Admission ControL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Burst Ad mission Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ ..................... . .......... 27

v

Usage Parameter Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Traffic Shaping and Rate Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Cell Level Priority Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

T "

P' . ransml ssl on n on ty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Space Priority Control and Buffer Management. . . . . . . . . . . . . . . . . . . . . . . . . .

Congestion Notification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

III BANDWIDTH ALLOCATION WITH MULTIPLE

32

38

43

43

45

48

49

NESTED CHARGE CLASSES. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Preliminaries. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1

Allocation Problem for Two Charge Classes : An Intuitive Ar gument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Generalization for More than Two Classes : Expected Marginal Bandwidth Revenue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1

Basics of Optimal Threshold Levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Notations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Revenue Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Properties of Revenue Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Op timal Threshold Policies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Optimality of Fixed Threshold Level Booking Policies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Alternative Expression fo r Optimal Thresh old Levels . . . . . . . ..... ...... 74 Numerical Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . 77

Conclusion and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

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IV NEURAL BASED CALL ADMISSION CONTROL AND CHARACTERIZING VARIATION OF TRAFFIC PARAME TERS. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1

Traffic Mod el Description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

QOS Metrics for Call Ad mission. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

QOS metric I : Buffer Overflow Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

QOS Metric II: Virtual Cell Loss Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

QOS Metric III: Ind ivid ual Virtual Cell Loss Probability . . . . . . . . . . . . . 89

The Neural Network Mod el. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1

Neural Network Schemes for Call Ad mission Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Scheme I: NN based Ad mission Contf01 with VCLP . . . . . . . . . . . . . . . . 93

Scheme II: N N based Ad mission Control with IVCLP. . . . . . . . . . . . . . 95

Numerical Results for Scheme I and Scheme II . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Scheme III: Neural Network Scheme with Equivalent Capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 00

Simulation and Experimental Results for Scheme 111................... 1 02

Characterizing Variation of Traffic Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 05

The NN Scheme for Mod eling Variation of Traffic Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 08

Rem ar ks: Application Scenar ios in Band wid th All ocation. . . . . . . . . . 11 0

Simulation and Experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 1

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 2

V SU MM ARY AND CONCLU SIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 5

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 5

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Limitations of the Study. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 118 Future Research and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 120

BIBLIOGRAPHy . . . . . . .. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . 123 BIOGRAPHICAL SKE TCH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . 139

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LIST OF TABLES

Table Page

1 Comparison Between MLT and QLT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . 44 2 Percentage of Bad Acceptance of Calls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

3 Accuracy ofNN Schemes Trained with Different Size of Training Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . 112

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LIST OF FIGURES

Figure Page

1 STM and ATM Principles ................................................................................ . 3

2 Schematic Illustration of Virtual Paths ............................................................. . 4

3 Structure of a Typical A TM Switch ................................................................ .. 5

4 An A TM Network Consisting of a Number of Switches Connected in an Arbitrary Topology . . . . . . . . . . . . . .. . .. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . .. .. .. . . . . . . . . . . . . . . . . 6

5 Spectrum and Classification of ATM Traffic Control Methods for Various Time-Scaled Traffic Entities ............................................................................ . 16

6 Concept of VP Bandwidth Control. .................................................................. . 20

7 Algorithm Steps for Admittting New Connections., .......................................... . 23

8 Fast Bandwidth Reservation ............................................................................ . 30

9 Jumping Window Scheme ................................................................................ . 33

10 Worst case Jumping Window Scheme .............................................................. . 33

11 Triggered Jumping Window Scheme ................................................................ . 34

12 Moving Window Scheme ................................................................................. . 34

13 Worst Case Moving Mindow Scheme ............................................................. . . 35

14 Model of Leaky-Bucket Bandwidth Enforcement Strategy ................................ . 36

15 Neural Network Traffic Enforcement Mechanism ............................................. . 37

16 Functional Block Diagram of Traffic Shaping .................................................. . 39

17 Traffic Shaping Flow-Chart ............................................................................. . 39

18 Input/Output Cell Sequence of Traffic Shaper .................................................. . 40

19 Synchronization Between Input and Output Links in Stop-and-Go .................... . 41

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20 Polling Schenle ..... . . . . .... . . . . . . . .. . . . . .. . . . . . . . . . ... . . . . . . .. . . . ..... . ............. . ............. ......... . . . . 45

21 Pushout Scheme ............................................................................................. .. 47 22 Explicit Congestion Notification Scheme ......................................................... . 48

23 Hierarchical Control System Structure for Bandwidth Allocation .................... .. 58

24 Threshold Based Bandwidth Allocation Scheme .............................................. . 60

25 ON-OFF Traffic Model .................................................................................. . 86

26 Virtual Cell Loss Probability Concept. .......................................................... .. 88

27 Individual Virtual Cell Loss Probability Probability Concept.. ....................... .. 90

28 Typical Structure for a Back-Propagation Networ� ....................................... .. 92

29 Sigmoidal Function ........................................................................................ . 92

30 NN Based CAC by VCLP Estimation Method ............................................... .. 94

31 Multi-input! Multi-output NN Structure to Guarantee Individual Call Quality ................................................................................. .. 95

32 Acceptance Regions ...................................................................................... . 96

33 CPU Time Versus No. Of Calls ................................................................... .. 99

34 Structure of the Proposed NN Based Adaptive CAC Method ......................... . 101

35 Equivalent Capacity for a Single Source ....................................................... . . 102

36 Equivalent Capacity for 5 Sources ................................................................ . 103

37 Equivalent Capacity for 50 Sources ............................................................. . . 104

38 Variation of Traffic Parameters of a Connection .......................................... . . 105

39 Distortion of Traffic Pattern due to Load Fluctuations ................................. .. 106

40 Simulation Configuration to Observe Traffic Changes .................................. . 107

41 The NN Model to ,Characterize Variation of Traffic Parameters ................... .. 109

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LIST OF ABBREVIATIONS

ABR Available-Bit-Rate

ATM Asynchronous Transfer Mode

B-ISDN - Broadband Integrated Services Digital Network

BPNN Back-Propagation Neural Network

CAC Call ( Connection ) Admission Control

CBR Constant-Bit-Rate

CLP Cell Loss Probability

CLR Cell Loss Ratio

DT Delayed Transmission

ECN Explicit Congestion Notification

EWMA - Exponentially Weighted Moving Ave ... 0 ....

FCI Forward Congestion Indicator

FRP Fast Reservation Protocol

HRR Hierarchical Round Robin

IP Internet Protocol

IPP Interrupted Poisson Process

IVCLP Individual Virtual Cell Loss Probability

IT Immediate Transmission

JW Jumping Window

LB Leaky Bucket

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LAN Local area Network

ML T Maximum Laxity Threshold

MMPP Markov Modulated Poisson Process

MV Moving Window

NN Neural Network

NNTEM - Neural Network Traffic Enforcement Mechanism

PBR Peak- Bit- Rate

PBS Partial Buffer Sharing

QLT Queue Length Threshold

QOS Quality of service

SE Switching Element

SQ Source Quench

STM Synchronous Transfer Mode

TDM Time Division Multiplexing

TCP Transmission Control Protocol

TJW Triggered Jumping Window

UPC Usage Parameter Control

VBR Variable Bit Rate

VC Virtual Channel

VCI Virtual Channel Identifier

VCLP Virtual Cell Loss Probability

VP Virtual Path

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VPI Virtual Path Identifier

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Abstract of the thesis submitted to the Senate of Universiti Pertanian Malaysia in fulfillment of the requirements for the degree of Master of Science .

TRAFFIC CONTROL IN ASYNCHRONOUS TRANSFER MODE NETWORKS

By

Ibrahim Khalil

September 1 995

Chairman: Associate Professor Dr. Borhanuddin Mohd Ali Faculty : Engineering

In the 90s, there is an increasing demand for new telecommunication services such

as video conferencing, videophone, broadcast television, image transfer and bulk file

transfer etc. At the same time, transmission systems at bit rates of 2.5 Gb/s are now

being installed, and the expected next generation of 1 0 Gb/s systems is emerging from the

research laboratories. Coupled with that the development and deployment of new

technologies systems such as fiber optics and intelligent high-speed switches have made it

possible to provide these services in future high-speed integrated services networks like

Asynchronous Transfer Mode (ATM). However, because of their new characteristics,

these new services pose great challenges not previously encountered in traditional circuit-

switched or packet switched networks. For example, features such as large propagation

delay as compared to transmission delay, diverse application demands, constraints on call

processing capacity, and Quality-Of-Service (QOS) support for different applications all

present new challenges arising from the new technology and new applications. Thus, much

research is needed not just to improve existing technologies, but to seek a fundamentally

different approach toward network architectures and protocols. In particular, new

xv

bandwidth allocation and call admission control algorithms need to be studied to meet

these new challenges.

A VP bandwidth allocation problem is studied for services which requires

guaranteed connection for a fixed duration of time leading to extensive use of facilities like

reservations of transmission capacity in advance. In such a case, the network may offer

discounts for users reserving capacities in advance due to the advantage of working with

predetermined traffic loads. Similarly, charges may differ for customers wanting to book

capacity for a specified ti�e interval. Based on this scenario, various charge classes and

booking poliCies are introduced. An effective bandwidth allocation scheme is proposed at

the VP level with multiple nested charge classes where these various classes are allocated

bandwidth optimally through some booking policies.' The scheme is also shown to be

effective in maximizing network revenue. The best tradeoff between revenue gained

through greater demand for discount bandwidth units against revenue lost when full­

charge bookings request must be turned away because of prior bookings of discount

bandwidth units is also sought for.

To increase the average utilization of the network by real-time traffic, new

Neural Network (NN) based Call Admission Control (CAC) schemes of various

capabilities are introduced. These schemes are able to adapt gracefully to the dynamic

behaviour of traffic and time-varying nature of network conditions, especially when new

services are being continually introduced after network design and installation. Since

traffic parameters in an ATM network may change over time from the values declared at

the connection setup, a Neural Network approach is proposed to effectively characterize

the changes in traffic parameters declared during call set-up and establish a simple input-

xvi

output relation from which values of output traffic parameters can be produced. These

new parameters can be used for bandwidth allocation in the node downstream.

xvii

Abstrak tesis yang dikemukakan kepada Senat U niversiti Pertanian Malaysia untuk memenuhi keperluan Jjazah Master Sains

KAWALAN TRAFIK DALAM RANGKAIAN MOD PEMINDAHAN TAKSEGERAK

Oleh

Ibrahim Khalil

September 1 995

Pengerusi: Prof Madya Dr. Borhanuddin Mohd. Ali Fakult i : Kejuruteraan

Dalam dekad ke '90an, terdapat peningkatan permintaan untuk perkhidmatan

telekomunikasi baru seperti persidangan video, telefon video, televisyen penyiaran,

pemindahan imej dan pemindahan besar fail. Pad a masa yang sarna sistem penghantaran

dengan kadar bit 2-5 Gb/s sedang dipasangkan manakala sistem generasi "masa depan" 1 0

ObIs mula muncul dari makmal pcnyclidikan. Di samping itll kcmunculan suis pantas

pintar telah memungkinkan penawaran perkhidmatan tersebut dalam rangkaian pantas

terkamir masa depan sepert i Mod Pemindahan Taksegerak (Asynchronous Transfer Mode,

ATM). Walau bagaimanapun, oleh kerana cirinya yang baru, perkhidmatan ini

menimbulkan cabaran yang belum pernah dihadapi dalam rangka ian tradisional pengsuisan

l itar atau pengsuisan paket, seperti kelengahan perambatan yang tinggi berbanding dengan

kelcngahan penghantaran, permintaan aplikasi yang berbagai, pengekangan pada

kcupayaan pemproscsan panggilan dan sokongan kcpada kualiti perkhidmatan untuk

aplikasi yang berbagal . Oleh itu, tesis ini mengkaji algoritma pcnguntukan lebarjalur dan

kawnlnn kchcnnran Illasuk panggilan u nt llk mcnyahut caharan ini

xviii

Masalah pengagihan Laluan Maya, (Virtual Path,VP) adalah dikaji untuk

perkhidmatan yang memerlukan sambungan terjamin untuk jangka masa yang tetap

menjurus kepada penggunaan kemudahan yang meluas seperti penempahan keupayaan

awal . Dalam kes ini, rangkaian tersebut boleh menawarkan diskaun untuk pengguna yang

menempah kapasit i terlebih dahulu kerana manafaat berurusan dengan beban l alu-lintas

yang ditentukan terlebih dahulu. Begitu juga, bayaran boleh berbeza untuk pelanggan yang

ingin menempah kapasiti untuk satu jangka masa yang tertentu. Berdasarkan kepada I

senario ini, beberapa kelas pembayaran dan polisi tempahan diperkenalkan. Suatu skim

penguntukan lebaijalur di peringkat VP dengan kelas bayaran bergelung berbilang, melalui

polisi penempahan yang ditunjukkan sebagai optimum di kalangan kelas bayaran tersebut

dan berkesan dalam memaksimumkan hasil rangkaian, juga diperihalkan. Penggantian

yang paling baik juga dicari di antara hasil pungutan yang diterima melalui permintaan

yang meningkat untuk lebarjalur diskaun ke atas hasil pungutan yang luput apabila

penempahan bayaran penuh terpaksa ditolak kerana terdapat penempahan lebarjalur

diskaun terlebih awal .

Untuk meningkatkan penggunaan purata rangkaian olch lalu-lintas masa-nyata,

suatu skim pembenaran-masuk panggilan Rangkaian Neural telah diperkenalkan. Skim ini

mampu menyesuaikan dengan berkesan kelakuan lalu-lintas yang dinamik dan keadaan

sebenar rangkaian yang berubah mengikut masa, terutama sekali apabila perkhidmatan

baru diperkenalkan setelah rekabentuk rangkaian dan pemasangannya selesai dibuat . Oleh

kerana parameter lalu-lintas dalam rangkaian A TM mungkin berubah daripada nilai yang

dimaklumkan pada peringkat penyambungan panggilan suatu pendekatan Rangkaian

Neural telah dicadangkan untuk mencirikan perubahan parameter lalu-lintas tersebut yang

dideklarasikan pada peringkat penyambungan panggilan tadi, dan seterusnya menetapkan

suatu hubungan masukanlkeluaran mudah yang mana nilai parameter lalu-lintas keluaran

xix

dapat diperolehi darinya. Parameter baru ini boleh digunakan untuk peruntukan lebarjaJur

di nod seterusnya.

xx

CHAPTER I

INTRODUCTION

Communications systems have been revolutionized by technological advances in

the last decade. The speed and capacity of various components in a communication I

system, such as transmission media, switches, memory, processors, have all followed

technological curves that have grown either linearly or exponentially over the last ten

years (Fraser, 1 99 1). At the periphery of the network, driven by the same underlying

technology- microelectronics, the capability of computers has been drastically increased

while the cost has been significantly reduced. To take advantage of the technological

trends, and to satisfY the growing need for people and computer to communicate, new

integrated services networks are being designed. Unlike the current communications

networks which are designed to offer a special type of service, the integrated-services

network like Asynchronous Transfer Mode (ATM) will offer multiple services that include

data, voice, video, and others. The integration of multiple services into one network will

offer a number of advantages, including vast economies of scale, ubiquity of access, and

improved statistical multiplexing. However, this integration also presents new challenges

for nelwork designers.

Traditional communication networks offer a single type of service that supports

one type of applications. For example, since the telephone network has been designed to

support interactive voice, which requires a low delay, low rate, fixed bandwidth, two-way,

jitter-free service. Alternatively, the cable TV network has been designed to support

broadcasting or analog video, which requires a high rate, fixed handwidth, onc-way, jiller-

free service. Finally, the data network has been designed to support communication

between computers. Although Quality-Of-Service (QOS) has been considered, the current

1

2

data network only offers a best effort service - there are no guarantees on performance I

parameters such as delay or throughput. The specialization of each of these networks has

allowed the network lfp.�ion to be optimized for that specific type of service.

ATM will have to support applications with diverse traffic characteristics and

performance objectives. There have been many applications proposed, and there is no

doubt that many more will emerge once the networks are in place. These applications

include digital television, digital audio and facsimile transmission, and in general

multimedia applications. The QOS expected from the network by these applications varies

over a wide range : some are sensitive to delays experienced in the communication

network, others are sensitive to loss rates, while yet others are sensitive to delay

variations. An ATM network, which aims to support all these services, must attempt to

meet the needs of all these applications.

A TM Network Environment

In an A TM network, the transfer of information is made in the form of cells of

fixed length, regardless of the information conveyed. Operation is asynchronous, in the

sense that cells do not have an identified position in time unlike the bytes of a

synchronous link that occupy a fixed position within a frame. All communication in an

A TM network is connection-oriented, i . e., a connection needs to be established before

data transmission can begin. The asynchronous transfer mode is a more flexible

alternative to the Jynchronous transfer mode (STM), in which ce]Js belonging to a

connection could only occupy certain predetermined time-slots on a transmission link.

'fhe diflcrcncc between the two modcs is illustrated by l'igurc 1, which shows some

possible patterns of slot usage in the two modes. Four connections are assumed to exist.

3

In STM, time is divided into frames (marked by the shaded cells in the figure), and each

connection gets one or more slots in a frame to transfer its cells. Thus connection 2 can

only transfer cells in the first slot of a frame, connection 3 in the second slot, connection 4

in the 3rd slot and connection 1 in the fourth slot. In the central frame shown at the top of

the figure, the slot for connection I is wasted because there is no cell to use it. In ATM,

cells are allowed to use the slots as soon as they are available, and so the cell belonging to

connection 2 can go ahead in the empty slot.

A TM: cells can be filled up on demand header

VC stands for virtual channel number

Figure 1 : STM and A TM Principles

An A TM logical channel is identified by means of two hierarchical entities: the

virtual path (VP) and the virtual channel (VC), each having a sub field in the header of

every ATM cell, the virtual path identifier (VPI) and the virtual channel identifier

(VCI). The two identifiers are used to establish a virtual link across the interfaces of an

ATM connection. The virtual paths allow several virtual channels to be multiplexed or

switched in a single block. In an ATM crossconnect network, the crossconnects (or

4

VP-switches) only handle the virtual path identifiers. Several VPs may be multiplexed

on the same physical link. Figure 2 shows the concept of virtual paths. There are three

virtual paths, VPI, VP2 and VP3, shown in the figure. For example, VP2 is a virtual

path between end nodes Nt and N5. N3 and N4 are transit nodes of virtual path VP2.

N3 is a crossconnect.

Node

VPt Physical Link

Virtual Circuit

Virtuall)ath

Figure 2 : Schematic Illustration of Virtual Paths

An ATM network consists of a number of ATM switches connected in an

arbitrary mesh. It is assumed that each switch is accompanied by a switch manager,

which is responsible for accepting or rejecting channels through the switch. The switch

manager should be running the algorithms required to establish connections and

perform any admission control tests required to ensure the quality of service needed by

individual connections. When a channel is c�tahlished, the switching fabric maps cells

arriving along that channel from the input link to the appropriate output link. The

exact details of this mapping mechanism is not a concern here. Any of the the common

switching techniques may be used for the mapping operation. These techniques include

shared-memory switching (Condruse, 1987), shared medium switching (Gopal, 1987)

or space division switching fabrics (like banyan switches) (Hui, 1987). A survey of

switching schemes can be found in some recent literature (Ahmadi, 1989; Tobagi,

1990). In this thesis, no assumptions are made regarding the switching scheme used,

as long as the switch model proposed below is valid.

An ATM switch can be modeled as a set of input queues terminating the

incoming links, connected through an interconnection network to a set of output queues

(outgoing links). Usually the only purpose of the input queues is cell/bit

synchronization and clock recovery; so input queue size is small. Nevertheless, it

should be noted that statistical switching techniques will necessitate large input queue

sizes in some cases. The output queues are needed since cells from different incoming

links that are to be routed towards the same outgoing link may arrive concurrently.

Each switch can be represented as in Figure 3.

input links output links

input buffer output buffer

Figure 3: Structure of a Typical ATM Switch