Preface | p. v |
About the Editors | p. ix |
List of Contributors | p. xv |
Performance Design and Engineering | |
Machine Learning Techniques-Reductions Between Prediction Quality Metrics | p. 3 |
Introduction | p. 3 |
Basic Definitions | p. 4 |
Importance-Weighted Classification | p. 5 |
Multiclass Classification | p. 10 |
One-Against-All | p. 10 |
Error-Correcting Coding (ECOC) Approaches | p. 13 |
Approaches Based on Pairwise Comparisons | p. 17 |
Cost-Sensitive Classification | p. 17 |
Predicting Conditional Quantiles | p. 20 |
Ranking | p. 25 |
Conclusion | p. 26 |
References | p. 26 |
Performance Engineering and Management Method - A Holistic Approach to Performance Engineering | p. 29 |
Background | p. 29 |
What is Performance Engineering? | p. 31 |
Overview of PEMM | p. 32 |
PEMM Theme - Requirements and Early Design | p. 33 |
Requirements and Performance | p. 34 |
Early Design and Performance | p. 35 |
PEMM Theme - Volumetrics | p. 36 |
Business Volumes | p. 36 |
Technical Volumes | p. 37 |
PEMM Theme - Estimation and Modeling | p. 38 |
Performance Estimating Techniques | p. 39 |
Selection of Performance Estimating Methods | p. 41 |
PEMM Theme - Technology Research | p. 42 |
PEMM Theme - Design, Development and Tracking | p. 42 |
Recognizing Performance Patterns and Anti-Patterns | p. 43 |
Designing for Performance | p. 44 |
Performance Budgeting | p. 44 |
Performance Debugging and Profiling | p. 45 |
Design, Development and Tracking Guidance | p. 46 |
PEMM Theme - Test Planning and Execution | p. 46 |
PEMM Theme - Live Monitoring and Capacity Planning | p. 47 |
Relating PEMM to Performance and Capacity Management | p. 49 |
PEMM and ITIL Capacity Management | p. 50 |
PEMM Theme - Performance and Risk Management | p. 51 |
Assignment of Dedicated Performance Engineering Resources | p. 52 |
Applying PEMM to IT Project Governance | p. 52 |
Applying PEMM to Complex Projects | p. 53 |
Summary | p. 54 |
References | p. 55 |
Economic Models of Communication Networks | p. 57 |
Introduction | p. 57 |
General Issues | p. 58 |
Paris Metro Pricing | p. 60 |
Pricing of Services | p. 63 |
Tragedy of the Commons | p. 64 |
Congestion Pricing | p. 66 |
When to Use the Network? | p. 66 |
Service Differentiation | p. 69 |
Auctions | p. 73 |
Investment Incentives | p. 78 |
Free Riding | p. 78 |
Network Neutrality | p. 81 |
Economics of Security | p. 84 |
Conclusions | p. 86 |
References | p. 87 |
Algorithmic Methods for Sponsored Search Advertising | p. 91 |
Introduction | p. 91 |
Existing Auctions | p. 93 |
Practical Aspects | p. 95 |
The Advertiser's Point of View: Budget Optimization | p. 97 |
Modeling a Keyword Auction | p. 99 |
Uniform Bidding Strategies | p. 104 |
Experimental Results | p. 105 |
Extensions | p. 105 |
The Search Engine's Point of View: Offline Slot Scheduling | p. 106 |
Special Case: One Slot | p. 108 |
Multiple Slots | p. 110 |
Extensions | p. 113 |
The User's Point of View: a Markov Model for Clicks | p. 114 |
A Simple Markov User Click Model | p. 116 |
Properties of Optimal Assignments for Markovian Users .117 | |
Computing the Optimal Assignment | p. 118 |
Open Issues | p. 118 |
Concluding Remarks | p. 119 |
Acknowledgements | p. 120 |
References | p. 120 |
Scheduling and Control | |
Advances in Oblivious Routing of Internet Traffic | p. 125 |
Introduction | p. 125 |
The Need for Traffic Oblivious Routing | p. 127 |
Difficulties in Measuring Traffic | p. 127 |
Difficulties in Dynamic Network Reconfiguration | p. 128 |
Traffic Variation and Performance Models | p. 128 |
Unconstrained Traffic Variation Model | p. 128 |
Hose Constrained Traffic Variation Model | p. 129 |
Oblivious Routing under Unconstrained Traffic Model | p. 130 |
Oblivious Routing of Hose Constrained Traffic | p. 131 |
Two-Phase (Oblivious) Routing of Hose Constrained Traffic | p. 132 |
Addressing Some Aspects of Two-Phase Routing | p. 135 |
Benefits of Two-Phase Routing | p. 137 |
Determining Split Ratios and Path Routing | p. 138 |
Protecting Against Network Failures | p. 139 |
Generalized Traffic Split Ratios | p. 140 |
Optimality Bound for Two-Phase Routing | p. 141 |
Summary | p. 143 |
References | p. 144 |
Network Scheduling and Message-passing | p. 147 |
Introduction | p. 147 |
Model | p. 150 |
Abstract formulation | p. 150 |
Scheduling algorithms | p. 151 |
Input-queued switch | p. 152 |
Wireless networks | p. 153 |
Characterization of optimal algorithm | p. 155 |
Throughput optimality | p. 156 |
Queue-size optimality | p. 159 |
Message-passing: throughput optimality | p. 165 |
Throughput optimality through randomization and message-passing | p. 165 |
Performance in terms of queue-size | p. 169 |
Message-passing: low queue-size or delay | p. 172 |
Input-queued switch: message-passing algorithm | p. 172 |
Wireless scheduling: message-passing scheduling | p. 178 |
Discussion and future direction | p. 182 |
References | p. 183 |
Introduction to Control Theory And Its Application to Computing Systems | p. 185 |
Introduction | p. 185 |
Control Theory Fundamentals | p. 186 |
Application to Self-Tuning Memory Management of A Database System | p. 191 |
Application to CPU Utilization Control in Distributed Real-Time Embedded Systems | p. 197 |
Application to Automated Workload Management in Virtualized Data Centers | p. 201 |
Introduction | p. 201 |
Problem statement | p. 203 |
Adaptive optimal controller design | p. 203 |
Experimental evaluation | p. 205 |
Application to Power and Performance in Data Centers | p. 207 |
Design Methodology for Integrating Adaptive Policies | p. 207 |
Evaluation | p. 211 |
Conclusions And Research Challenges | p. 212 |
References | p. 214 |
Index | p. 217 |
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