Multicast Rout ing and Resource Allocation for High - T-Space

148 Pages·1999·7.97 MB·English

Multicast Rout ing and Resource
Allocation for High-Speed Networks
A thesis submitted in conformiw with the requirements
for the Degree of Doctor of Phîlosophy,
Depart ment of Elect rical and Computer Engineering,
at the University of Toronto
@ Copyright by Sanjeev Verma 1998
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TO My Parents and to the loving memory of late younger brother
Multicast Routing and Resource Allocation for
High-Speed Networks
Sanjeev Verma
Degree of Doctor of Philosophy
Department of Electncal and Cornputer Engineering
University of Toronto
Abstract
In recent years, a number of architectures (ATM, Next Generation Internet) have
been propoûed to provide real-tirne multimedia services over packet switched net-
works. Most of the emerging services need certain "quality of service" (&os) guar-
antees fiom the network. Ehrthermore, many of these applications are multicast in
nature. Five important components of the emerging network architecture supporting
these applications are: Flow specification, Routing, Resource Reservation, Admission
Control and Packet Scheduling. These components in conjunction tly to meet the
QoS requirements of a wide range of services. The goal is not just meeting &os
requirements of the applications but doing so efficiently. There is a strong interac-
tion between various components of the architecture and hence the design of any
component is based on certain assumptions about the remaining components of the
architecture. This dissertation addresses the issues involved in the design of rout-
hg, resource allocation and c da dmission control components of the next generation
Intemet. The approach is however general in nature and applicable to any high
speed integrated packet switched network. We particularly concentrate on guaran-
teed quality of service (G QoS) applications, defied by IETF (Intemet Engineering
Task Force), that need a he nd-to-end delay bound and no queuing loss for con-
forming packets. Conceptuaily we cm think that a connection set-up process consists
of two phases, though physical implementation may be Merent. In the first phase, a
route is established fkom the source node to the destination node. The second phase
consists of checking whether or not the application can be admitteci dong the route to
meet the user's requirements- The success of the cal1 admission phase depends on the
amount of resources ( buffer, bandwidth etc.) available in the selected route by the
routing process in the first phase. A related issue is the mapping of end-to-end QoS
requirement into local QoS requhments and then mapping local QoS requirements
into resource requirements.
in this dissertation, we first give an efficient and distributed algorithm based on
the cost function ( function of node utilization) to divide the end-to-end guaranteed
QoS requirements into local QoS requirements. The simulation results show that
the QoS division scheme baed on cost function results in an efficient utilization of
resources compared to the equal division scheme. We then select suitable routing
metrics for GQoS applications in order to make an efficient use of network resources.
We then give a heuristic approach that use these metrics to produce sub-optimum
multicast trees. We also show that the resource resemtion scheme given for uni-
cast connections can also be used with multicast connections to do efficient resource
reservation. We note that the key idea with both resource reservation and QoS rout-
h g is the efficient distribution of load acrosç the network. Since both routing and
resource reservation share the same goal, an efficient design is possible if these two
problems are addressed concurrently. We show that the performance of a given re-
source reservation mechanism depends strongly on the underlying routing algorithm.
Our simulation results show that the QoS routing algorithm proposed in this disser-
tation gives very good performance when used with cost based resource reservation
scheme. We find that this is due to the even distribution of load across the network
and minimal consumption of resources that result when this particular combination
is used.
Acknowledgments
Several people supported my efforts during rny graduate years and 1 would like
to thank them. First, 1 would like to thank Leon Garcia for introducing me to new
results and open issues in the exciting world of high speed networks. Hiç feedback
helped immensely in setting a definitive direction and improving the quality of my
thesis.
Jerry Hayes spent hours driving into me what research is all about. He taught by
his own example the importance of long term vision in research.
I thank Rajesh Panlraj for his everlasting enthUSIasm towards new pro blems. Reg-
dar discussions with Pankaj and his feedback helped greatly in improving the tech-
niques presented in this thesis.
Anindo Banerjea provided very useful feedback, which improved the presentation
of the techniques in this thesis. 1 thank the rest of my cornmittee, A.N. Venetsanopou-
los, Km Sevcik and Gien Gulak for carefully reading through the thesis and providing
helpful suggestions.
I would like to thank my yoga teacher Axe1 Molema and other friends from Hart
House Yoga Club for keeping me physically, mentally and spiritually in a good health
during my stay in University of Toronto.
1 would like to thank Sanjay La1 and Suhas Mansingh for help with simulations and
many a tea-time chats. 1 would also like to thank my innumerable friends, including
Nikhilesh Swamy, Hassan Naser, M. Mahmoudi, S. K. Chan, Massoud Khansari,
Rathneshwaran, Ragulan, Alagan, Hashemi, and several others missing hom this List,
for making my graduate school stay more enjoyable than it sometimes seemed to be.
Special thanks go to Sarah Cherian for going out of the way to squeeze my thesis
defense in a very tight schedule.
My research would not have been possible without the generous support of the
Canadian Commonwealth Fellowship, NSERC and Nortel.
Cousin Ritu pepped me up when the morale was low. 1 thank my parents, sister
and brother-in-law for constant encouragement and moral support during all these
years of study, that at times seemed endless. Last but not the lest, my late brother
who was always the first to encourage me in anything that I ever tned to accomplish.
Contents
Abstract
Acknowledgements
Contents
List of Figures
List of Tables
1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Background and Related Work . . . . . . . . . . . . . . . . . . . . . .
1.2.1 Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . .
1.2.2 QoS Routing . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.3 Multicast QoS Rauting . . . . . . . . . . . . . . . . . . . . . .
1.3 Main Contributions of this Dissertation . . . . . . . . . . . . . . . . .
1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . .
2 Cal1 Admission and Resource Allocation
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Resource Resemtion Protocol (RSVP) . . . . . . . . . . . . . . . . .
2.3 End-to-End Delay Bound . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Problem Description and Formulation: . . . . . . . . . . . . . . . . .
2.5 End-to-End QoS Divison Policies . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . .
2.5.1 Equal Division Policy 31
. . . . . . . . . . . . . . . . . . .
2.5.2 Cost Based Division Policy 31
. . . . . . . . . . . . . . . . . . . . . . . . .
2.6 An Improved Algorithm 38
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7 Multicast Sgsion 39
2.7.1 Static Multicaçt Problem . . . . . . . . . . . . . . . . . . . . . 41
. . . . . . . . . . . . . . . . . . .
2.7.2 Dynamic Multicast Problem 42
. . . . . . . . . . . . . . . . . . . . . . . . . .
2.8 A Numerical Example 44
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9 Simulation Results 46
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.10 Summary 50
3 Multicast Quality of Service Routing 52
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 Introduction 52
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Dekitions 56
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Problem Statement 58
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Metric Selection 59
3.4 Path Selection Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 62
. . . . . . . . . . . . . . . . . . . . . .
3.4.1 Shortest-Path heuristic 62
3.4.2 Nearest Destination First Heuristic . . . . . . . . . . . . . . . 66
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Experimental Setup 71
. . . . . . . . . . . . . .
3.6 Perfomance Metric and Simulation Results 72
3.6. i Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 73
. . . . . . . . . . . . . . . . . . . . . .
3.6.2 Discussions of Results 84
3.6.3 Effect of distribution of degree across the network . . . . . . . 90
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7 Summary 90
4 Interaction between QoS Routing and Resource Allocation 91
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1 Introduction 91
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Problem Definition 92
4.3 Performance of Cost vernis Equal Allocation Policy . . . . . . . . . . 95
. . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Numerical Example 98
. . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 QoS Routing schemes 100
5 Conclusions and Future Work 108
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2 F'utureWork.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
+
A Glossary 114
B List Of Symbols 116
C Kuhn-Tucker Optimality Conditions 118
D Verifications of Kuhn-Tucker Optimality Conditions 121
E Pseudocode for Nearest Destination First Heuristic 123
Bibliography 126