Utility-based Optimisation of Resource Allocation for Wireless

188 Pages·2005·1.39 MB·English

Linköping Studies in Science and Technology
Dissertation No. 963
Utility-based Optimisation of Resource Allocation for
Wireless Networks
by
Calin Curescu

Department of Computer and Information Science
Linköpings universitet
SE-581 83 Linköping, Sweden
Linköping 2005
Abstract
Wireless communication networks are facing a paradigm shift.
New generations of cellular and ad hoc networks are finding new
application areas, beitincommercial communication, formilitary
use or disaster management.
From providing only voice communications, wireless networks
aim to provide a wide range of services in which soft real-time,
high priority critical data, and best effort connections seamlessly
integrate. Some of these applications and services have firm re-
source requirements in order to function properly (e.g. video-
conferences), others are flexible enough to adapt to whatever is
available (e.g. FTP). Different connections might have different
importance levels, and should be treated accordingly. Provid-
ing differentiation and resource assurance is often referred to as
providing quality of service (QoS).
Inthisthesiswestudyhownovelresourceallocationalgorithms
can improve the offered QoS of dynamic, unpredictable, and re-
sourceconstraineddistributedsystems,suchasawirelessnetwork,
during periods of overload.
Weproposeandevaluateseveralbandwidthallocationschemes
in the context of cellular, hybrid and pure ad hoc networks. Ac-
ceptablequalitylevelsforaconnectionarespecifiedusingresource-
utilityfunctions,andourallocationaimstomaximiseaccumulated
system-wide utility. To keep allocation optimal in this changing
environment, we need to periodically reallocate resources. The
novelty of our approach is that we have augmented the utility
function model by identifying and classifying the way realloca-
i
ii
tions affect the utility of different application classes. We modify
the initial utility functions at runtime, such that connections be-
come comparable regardless of their flexibility to reallocations or
age-related importance.
Another contribution is a combined utility/price-based band-
width allocation and routing scheme for ad hoc networks. First
we cast the problem of utility maximisation in a linear program-
ming form. Then we propose a novel distributed allocation al-
gorithm, where every flow bids for resources on the end-to-end
path depending on the resource “shadow price”, and the flow’s
“utility efficiency”. Our periodic (re)allocation algorithms rep-
resent an iterative process that both adapts to changes in the
network, and recalculates and improves the estimation of resource
shadow prices.
Finally, problems connected to allocation optimisation, such
as modelling non-critical resources as costs, or using feedback to
adapt to uncertainties in resource usage and availability, are ad-
dressed.
Acknowledgements
ForemostIwouldliketothankmysupervisorSiminNadjm-Tehrani
forherconstantsupport,guidance, andcollaboration duringthese
five years. Her enthusiasm in addressing new challenges and her
goal oriented approach areexamples formeto follow. I would also
like to thank Jo¨rgen Hansson for the no-nonsense discussions and
for his insightful comments on my work and thesis.
Working together with other people provided sparks for new
ideas and finality for others. I would like to thank Teresa A.
Dahlberg and all my collaborators from academia and industry
for their inputs and the rewarding collaboration.
A big thanks goes to my colleagues at Link¨oping University,
especially to the RTSLAB members, for the lively discussions and
debates, and the pleasant work environment. My thanks also to
the administrative and technical staff for their prompt support.
Anne Moe has always been helpful, even when I left things for the
last minute.
CUGS, the Swedish National Graduate School in Computer
Science provided not only funds for my research but also oppor-
tunities for peer meetings and coursework. ECSEL, the Excel-
lence Center in Computer Science and Systems Engineering in
Linko¨ping, provided funding for my first research year.
I wish to salute all my friends, for making my life more en-
joyable, and for supporting me in many different ways, especially
Sorin for showing me this path. Last but not least, I am deeply
gratefultoDekaandmyfamilyfortheirloveandconstantsupport.
C˘alin Curescu
iii

Table of Contents
1 Introduction 9
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . 9
1.2 Problem description . . . . . . . . . . . . . . . . . 11
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . 13
1.4 Thesis outline . . . . . . . . . . . . . . . . . . . . . 15
1.5 List of Publications . . . . . . . . . . . . . . . . . . 16
2 QoS Preliminaries 19
2.1 CPU allocation in real-time systems . . . . . . . . 20
2.2 QoS in the Internet . . . . . . . . . . . . . . . . . . 22
2.3 Characteristics of a QoS architecture . . . . . . . . 25
2.3.1 QoS specification . . . . . . . . . . . . . . . 26
2.3.2 QoS enforcement mechanisms . . . . . . . . 27
3 Utility model 31
3.1 Application adaptation . . . . . . . . . . . . . . . . 31
3.2 Utility optimisation and fairness . . . . . . . . . . 33
3.3 Application utility . . . . . . . . . . . . . . . . . . 34
3.4 Q-RAM utility model . . . . . . . . . . . . . . . . 35
3.5 Optimising single resource allocation . . . . . . . . 38
3.6 Utility setup for network traffic . . . . . . . . . . . 42
4 Adaptive reallocation 49
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Related work . . . . . . . . . . . . . . . . . . . . . 52
1
2 TABLE OF CONTENTS
4.3 Application area . . . . . . . . . . . . . . . . . . . 54
4.4 Reallocation consequences . . . . . . . . . . . . . . 58
4.4.1 Flexibility classes . . . . . . . . . . . . . . . 60
4.4.2 Drop penalty . . . . . . . . . . . . . . . . . 61
4.4.3 Adaptation time . . . . . . . . . . . . . . . 61
4.5 Dynamic reallocation . . . . . . . . . . . . . . . . . 62
4.5.1 Age and class influence . . . . . . . . . . . 64
4.5.2 Drop penalty influence . . . . . . . . . . . . 67
4.5.3 Adaptation time influence . . . . . . . . . . 68
4.5.4 Algorithm overview . . . . . . . . . . . . . 69
4.6 Evaluation setup . . . . . . . . . . . . . . . . . . . 71
4.6.1 RBBS description . . . . . . . . . . . . . . 72
4.6.2 Traffic and simulation parameters . . . . . 73
4.7 Evaluation results . . . . . . . . . . . . . . . . . . 74
4.7.1 Comparison with basic maximisation . . . . 75
4.7.2 Comparison with RBBS . . . . . . . . . . . 76
4.7.3 Effects of convex hull approximation . . . . 77
4.7.4 QoS per application group . . . . . . . . . . 78
4.7.5 Choice of performance metric . . . . . . . . 78
4.7.6 Connection duration estimation . . . . . . . 80
4.7.7 Complexity considerations . . . . . . . . . . 81
4.8 Reallocation overhead . . . . . . . . . . . . . . . . 82
4.8.1 Reallocation control . . . . . . . . . . . . . 84
4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . 87
5 Non-critical resources as cost 89
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 90
5.2 Related work . . . . . . . . . . . . . . . . . . . . . 91
5.3 Allocation in hybrid networks . . . . . . . . . . . . 93
5.3.1 System model . . . . . . . . . . . . . . . . . 93
5.3.2 Optimal bandwidth allocation . . . . . . . . 95
5.3.3 Linear programming formulation . . . . . . 97
5.3.4 Hybrid-heuristic algorithm . . . . . . . . . 100
5.4 Evaluation setup . . . . . . . . . . . . . . . . . . . 101
5.5 Experimental results . . . . . . . . . . . . . . . . . 102
TABLE OF CONTENTS 3
5.5.1 Accumulated utility as performance . . . . 103
5.5.2 QoS preservation . . . . . . . . . . . . . . . 106
5.5.3 Time complexity . . . . . . . . . . . . . . . 108
5.5.4 Cost influence . . . . . . . . . . . . . . . . . 109
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . 109
6 Price-based allocation 111
6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 112
6.2 Related work . . . . . . . . . . . . . . . . . . . . . 113
6.3 Problem formulation . . . . . . . . . . . . . . . . . 115
6.3.1 Network model . . . . . . . . . . . . . . . . 115
6.3.2 Linear programming formulation . . . . . . 118
6.3.3 Dual form and optimal solution properties . 122
6.4 Distributed routing and allocation . . . . . . . . . 124
6.4.1 Bid construction . . . . . . . . . . . . . . . 125
6.4.2 Independent allocation . . . . . . . . . . . 125
6.4.3 Discussions . . . . . . . . . . . . . . . . . . 126
6.4.4 QoS routing . . . . . . . . . . . . . . . . . . 129
6.4.5 Mobility and clique construction . . . . . . 130
6.5 Simulation and results . . . . . . . . . . . . . . . . 131
6.5.1 Evaluation setup . . . . . . . . . . . . . . . 131
6.5.2 Comparison of allocation schemes. . . . . . 132
6.5.3 Experimental results . . . . . . . . . . . . . 134
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . 139
7 Dealing with Uncertainties 141
7.1 Adaptive load control . . . . . . . . . . . . . . . . 142
7.2 Related work . . . . . . . . . . . . . . . . . . . . . 143
7.3 Load control in 3G networks. . . . . . . . . . . . . 145
7.4 Algorithm design . . . . . . . . . . . . . . . . . . . 148
7.5 Evaluation setup . . . . . . . . . . . . . . . . . . . 152
7.6 Simulation results . . . . . . . . . . . . . . . . . . 154
7.7 Summary . . . . . . . . . . . . . . . . . . . . . . . 156
4 TABLE OF CONTENTS
8 Conclusions and Future Work 161
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . 161
8.2 Future work . . . . . . . . . . . . . . . . . . . . . . 165
Bibliography 167
List of Figures
3.1 R-U function quantisation . . . . . . . . . . . . . . 38
3.2 The convex hull optimisation algorithm . . . . . . 40
3.3 Optimal allocation order . . . . . . . . . . . . . . . 41
3.4 Utility function shape for application group 1 . . . 45
3.5 Utility function shape for application group 2 . . . 45
3.6 Utility function shape for application group 3 . . . 46
3.7 Utility function shape for application group 4 and 6 47
3.8 Utility function shape for application group 5 . . . 47
4.1 The UMTS Radio Access Network . . . . . . . . . 55
4.2 Replacement opportunity . . . . . . . . . . . . . . 65
4.3 Age modification for class I and II, with tage =5,
i
tmax=10, and actual bandwidth b =4 . . . . . . . 66
i i
4.4 Class II drop modification with Pdrop=8, tage=5,
i i
tmax=10, and actual bandwidth b =4 . . . . . . . 68
i i
4.5 ClassIIIadaptationmodificationwithA =5,I =4,
i i
Padapt=2, and actual bandwidth b =4 . . . . . . . 69
i i
4.6 The TARA (re)allocation algorithm . . . . . . . . 70
4.7 The TARA utility accounting algorithm . . . . . . 71
4.8 Accumulated utility . . . . . . . . . . . . . . . . . 75
4.9 Connection blocking probability . . . . . . . . . . . 79
4.10 Connection dropping probability . . . . . . . . . . 80
4.11 Differences due to estimated duration . . . . . . . 81
4.12 Number of reallocations comparison . . . . . . . . 83
4.13 Reallocation control algorithm . . . . . . . . . . . 85
5