demand forecast, resource allocation and pricing for multimedia delivery from the cloud

211 Pages·2013·5.47 MB·English

DEMAND FORECAST, RESOURCE
ALLOCATION AND PRICING FOR
MULTIMEDIA DELIVERY FROM THE CLOUD
by
Di Niu
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy,
Department of Electrical and Computer Engineering,
at the University of Toronto.
Copyright c 2013 by Di Niu.
�
All Rights Reserved.
Demand Forecast, Resource Allocation and Pricing
for Multimedia Delivery from the Cloud
Doctor of Philosophy Thesis
Edward S. Rogers Sr. Dept. of Electrical and Computer Engineering
University of Toronto
by Di Niu
May 6, 2013
Abstract
Videotra�cconstitutesamajorpartoftheInternettra�cnowadays. Yetmostvideode-
livery services remain best-e↵ort, relying on server bandwidth over-provisioning to guar-
antee Quality of Service (QoS). Cloud computing is changing the way that video services
are o↵ered, enabling elastic and e�cient resource allocation through auto-scaling. In
this thesis, we propose a new framework of cloud workload management for multimedia
delivery services, incorporating demand forecast, predictive resource allocation and qual-
ity assurance, as well as resource pricing as inter-dependent components. Based on the
trace analysis of a production Video-on-Demand (VoD) system, we propose time-series
techniques to predict video bandwidth demand from online monitoring, and determine
bandwidth reservations from multiple data centers and the related load direction policy.
We further study how such quality-guaranteed cloud services should be priced, in both
a game theoretical model and an optimization model.Particularly, when multiple video
providers coexist to use cloud resources, we use pricing to control resource allocation
ii
in order to maximize the aggregate network utility, which is a standard network util-
ity maximization (NUM) problem with coupled objectives. We propose a novel class of
iterative distributed solutions to such problems with a simple economic interpretation
of pricing. The method proves to be more e�cient than the conventional approach of
dual decomposition and gradient methods for large-scale systems, both in theory and in
trace-driven simulations.
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Dedicated to my parents
iv
Acknowledgments
First and foremost, I would like to sincerely thank my thesis supervisor, Professor
Baochun Li, for his invaluable guidance throughout my Ph.D. studies. I deeply appre-
ciate his strict training and precious advice in all aspects of my academic development,
which are life-long treasures for myself. I feel very fortunate to have Professor Li as my
supervisor, and cherish all the opportunities to explore my favourite research topics and
exert my own strengths. These would not have been possible without the unwavering
support of Professor Li. The dedication, diligence and enthusiasm he has for an aca-
demic career was contagious and motivational for me, especially during tough times in
my Ph.D. pursuit. I am also thankful for the excellent example Professor Li has provided
as a successful scholar and professor in computer engineering and science.
The members of the iQua research group have contributed immensely to both my
personal and professional time at the University of Toronto. The group has been a
source of good advice and collaboration as well as friendships. I am especially grateful
for the valuable discussions and collaborations with Chen Feng, Hong Xu and Zimu
Liu. Through the projects we worked on and the papers we coauthored, I very much
appreciated their enthusiasm, intelligence, and amazing abilities in mathematical and
experimental studies. I would like to thank all the other colleagues in the iQua research
group, for precious friendships that have supported me throughout the past four years:
Jin Jin, Wei Wang, Yuefei Zhu, Yiwei Pu, Yuan Feng, Junqi Yu, and Elias Kehdi. I
would also like to acknowledge the visiting students in our group: Fangming Liu, Anh
Tuan Nguyen, Zhi Wang, and Boyang Wang for the numerous nights we spent together
in the lab. Other past group members that I have had the pleasure to work alongside
of are graduate students Mea Wang, Chuan Wu, and Hassan Shajonia; visiting scholars
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Hui Wang and Ruixuan Li; and the numerous summer and rotation students who have
come through the lab.
Lastly, I would like to thank my parents for raising me with a passion for science and
engineering, and for their love and encouragement in all my pursuits. Thank you!
Di Niu
University of Toronto
May 6, 2013
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Contents
Abstract iii
iv
Acknowledgments v
List of Tables xii
List of Figures xviii
1 Introduction 1
1.1 Challenges in Multimedia Delivery Systems . . . . . . . . . . . . . . . . . 1
1.2 In the Era of Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Thesis Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Related Work 10
2.1 Measurement, Prediction and Learning in VoD Systems . . . . . . . . . . 10
2.2 Cloud Workload Management and Resource Allocation . . . . . . . . . . 13
2.3 Cloud Resource Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
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CONTENTS CONTENTS
2.4 Algorithms for Distributed and Parallel Optimization . . . . . . . . . . . 16
3 Forecast in Video-on-Demand Systems 18
3.1 Monitoring UUSee Video-on-Demand System . . . . . . . . . . . . . . . . 20
3.2 Some Prediction Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Modeling and Forecasting the Demand . . . . . . . . . . . . . . . . . . . 24
3.3.1 Population Prediction via Seasonal ARIMA Processes . . . . . . . 27
3.3.2 Forecasting the Aggregate Bandwidth Demand . . . . . . . . . . . 32
3.3.3 Inferring Initial Demand using Mixtures of Gaussians . . . . . . . 34
3.4 Predicting Peer Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5 Applications in a P2P System . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4 Volatility Prediction and Dimension Reduction 48
4.1 Modeling Demand Volatility . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Volatility Estimation and Resource Allocation . . . . . . . . . . . . . . . 52
4.2.1 Comparing Five Volatility Models . . . . . . . . . . . . . . . . . . 53
4.2.2 Utilization as a Volatility Indicator . . . . . . . . . . . . . . . . . 56
4.3 Reducing Volatility through Diversification . . . . . . . . . . . . . . . . . 57
4.4 Dimension Reduction using PCA . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5 Cloud Bandwidth Reservation for VoD Applications 70
5.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2 Load Direction and Bandwidth Reservation . . . . . . . . . . . . . . . . 75
5.2.1 The Optimal Load Direction . . . . . . . . . . . . . . . . . . . . . 77
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CONTENTS CONTENTS
5.2.2 Suboptimal heuristics with Limited Replication . . . . . . . . . . 81
5.3 Experiments based on Real-World Traces . . . . . . . . . . . . . . . . . . 83
5.3.1 A Channel Interleaving Scheme . . . . . . . . . . . . . . . . . . . 84
5.3.2 Algorithms for Comparison . . . . . . . . . . . . . . . . . . . . . 85
5.3.3 Assumption Validation . . . . . . . . . . . . . . . . . . . . . . . . 86
5.3.4 Predictive Auto-Scaling vs. Reactive Provisioning . . . . . . . . . 88
5.3.5 Theoretical Optimal vs. Replication-Limited Heuristics . . . . . . 91
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6 The Bandwidth Reservation Market 94
6.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.1.1 The Cloud and Tenants . . . . . . . . . . . . . . . . . . . . . . . 98
6.1.2 The Broker and Load Direction Matrix . . . . . . . . . . . . . . . 99
6.1.3 Bandwidth Pricing in the Presence of a Broker . . . . . . . . . . . 101
6.2 Main Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.3 Optimal Bandwidth Saving . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.3.1 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.4 Pricing Region in Controlled Markets . . . . . . . . . . . . . . . . . . . . 108
6.4.1 Broker Profit and Price Discounts in the Pricing Region . . . . . 113
6.5 Pricing in Free Markets: A Game Theoretical Analysis . . . . . . . . . . 115
6.5.1 Nash Equilibrium: Existence and Uniqueness . . . . . . . . . . . . 116
6.5.2 Market Properties and Equilibrium Bandwidth Costs . . . . . . . 120
6.6 Trace-Driven Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
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CONTENTS CONTENTS
7 Algorithmic Cloud Bandwidth Pricing 126
7.1 A New Tenant-Cloud Agreement . . . . . . . . . . . . . . . . . . . . . . 128
7.2 Pricing Towards Social Welfare Maximization . . . . . . . . . . . . . . . 132
7.2.1 An Equivalent Pricing Problem . . . . . . . . . . . . . . . . . . . 134
7.2.2 No Multiplexing vs. Multiplexing All . . . . . . . . . . . . . . . . 138
7.2.3 Economic Implications . . . . . . . . . . . . . . . . . . . . . . . . 140
7.3 Distributed Optimization with Coupled Objectives . . . . . . . . . . . . 141
7.4 A Pricing Framework for Distributed Optimization . . . . . . . . . . . . 143
7.5 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.5.1 Dual Decomposition and Gradient Methods . . . . . . . . . . . . 145
7.5.2 New Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.5.3 Relationship to the Nonlinear Jacobi Algorithm . . . . . . . . . . 150
7.6 Convergence Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7.6.1 Contraction Mapping . . . . . . . . . . . . . . . . . . . . . . . . . 152
7.6.2 Convergence of Algorithm 1 . . . . . . . . . . . . . . . . . . . . . 153
7.6.3 Convergence of Algorithm 2 . . . . . . . . . . . . . . . . . . . . . 158
7.6.4 Convergence of Gradient Methods for Dual Problem . . . . . . . . 160
7.6.5 Quadratic Programming: Convergence Speed Comparison . . . . 162
7.6.6 Handling General Coupled Objectives . . . . . . . . . . . . . . . . 165
7.7 Distributed Solutions to Cloud Pricing . . . . . . . . . . . . . . . . . . . 168
7.7.1 Equation Update . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
7.8 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
7.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
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