Figures xvii
Tables xxi
Equations xxiii
Introduction xxv
I BASICS 1
1 CLOUD COMPUTING 3
1.1 Essential Cloud Characteristics 4
1.2 Common Cloud Characteristics 6
1.3 But What, Exactly, Is Cloud Computing? 7
1.4 Service Models 9
1.5 Cloud Deployment Models 11
1.6 Roles in Cloud Computing 12
1.7 Benefi ts of Cloud Computing 14
1.8 Risks of Cloud Computing 15
2 VIRTUALIZATION 16
2.1 Background 16
2.2 What Is Virtualization? 17
2.3 Server Virtualization 19
2.4 VM Lifecycle 23
2.5 Reliability and Availability Risks of Virtualization 28
3 SERVICE RELIABILITY AND SERVICE AVAILABILITY 29
3.1 Errors and Failures 30
3.2 Eight-Ingredient Framework 31
3.3 Service Availability 34
3.4 Service Reliability 43
3.5 Service Latency 46
3.6 Redundancy and High Availability 50
3.7 High Availability and Disaster Recovery 56
3.8 Streaming Services 58
3.9 Reliability and Availability Risks of Cloud Computing 62
II ANALYSIS 63
4 ANALYZING CLOUD RELIABILITY AND AVAILABILITY 65
4.1 Expectations for Service Reliability and Availability 65
4.2 Risks of Essential Cloud Characteristics 66
4.3 Impacts of Common Cloud Characteristics 70
4.4 Risks of Service Models 72
4.5 IT Service Management and Availability Risks 74
4.6 Outage Risks by Process Area 80
4.7 Failure Detection Considerations 83
4.8 Risks of Deployment Models 87
4.9 Expectations of IaaS Data Centers 87
5 RELIABILITY ANALYSIS OF VIRTUALIZATION 90
5.1 Reliability Analysis Techniques 90
5.2 Reliability Analysis of Virtualization Techniques 95
5.3 Software Failure Rate Analysis 100
5.4 Recovery Models 101
5.5 Application Architecture Strategies 108
5.6 Availability Modeling of Virtualized Recovery Options 110
6 HARDWARE RELIABILITY, VIRTUALIZATION, AND SERVICE AVAILABILITY 116
6.1 Hardware Downtime Expectations 116
6.2 Hardware Failures 117
6.3 Hardware Failure Rate 119
6.4 Hardware Failure Detection 121
6.5 Hardware Failure Containment 122
6.6 Hardware Failure Mitigation 122
6.7 Mitigating Hardware Failures via Virtualization 124
6.8 Virtualized Networks 127
6.9 MTTR of Virtualized Hardware 129
6.10 Discussion 131
7 CAPACITY AND ELASTICITY 132
7.1 System Load Basics 132
7.2 Overload, Service Reliability, and Service Availability 135
7.3 Traditional Capacity Planning 136
7.4 Cloud and Capacity 137
7.5 Managing Online Capacity 144
7.6 Capacity-Related Service Risks 147
7.7 Capacity Management Risks 153
7.8 Security and Service Availability 157
7.9 Architecting for Elastic Growth and Degrowth 162
8 SERVICE ORCHESTRATION ANALYSIS 164
8.1 Service Orchestration Definition 164
8.2 Policy-Based Management 166
8.3 Cloud Management 168
8.4 Service Orchestration’s Role in Risk Mitigation 169
9 GEOGRAPHIC DISTRIBUTION, GEOREDUNDANCY, AND DISASTER RECOVERY 174
9.1 Geographic Distribution versus Georedundancy 175
9.2 Traditional Disaster Recovery 175
9.3 Virtualization and Disaster Recovery 177
9.4 Cloud Computing and Disaster Recovery 178
9.5 Georedundancy Recovery Models 180
9.6 Cloud and Traditional Collateral Benefits of Georedundancy 180
9.7 Discussion 182
III RECOMMENDATIONS 183
10 APPLICATIONS, SOLUTIONS, AND ACCOUNTABILITY 185
10.1 Application Configuration Scenarios 185
10.2 Application Deployment Scenario 187
10.3 System Downtime Budgets 188
10.4 End-to-End Solutions Considerations 197
10.5 Attributability for Service Impairments 201
10.6 Solution Service Measurement 204
10.7 Managing Reliability and Service of Cloud Computing 207
11 RECOMMENDATIONS FOR ARCHITECTING A RELIABLE SYSTEM 209
11.1 Architecting for Virtualization and Cloud 209
11.2 Disaster Recovery 216
11.3 IT Service Management Considerations 217
11.4 Many Distributed Clouds versus Fewer Huge Clouds 224
11.5 Minimizing Hardware-Attributed Downtime 225
11.6 Architectural Optimizations 231
12 DESIGN FOR RELIABILITY OF VIRTUALIZED APPLICATIONS 244
12.1 Design for Reliability 244
12.2 Tailoring DfR for Virtualized Applications 246
12.3 Reliability Requirements 248
12.4 Qualitative Reliability Analysis 256
12.5 Quantitative Reliability Budgeting and Modeling 259
12.6 Robustness Testing 260
12.7 Stability Testing 267
12.8 Field Performance Analysis 268
12.9 Reliability Roadmap 269
12.10 Hardware Reliability 270
13 DESIGN FOR RELIABILITY OF CLOUD SOLUTIONS 271
13.1 Solution Design for Reliability 271
13.2 Solution Scope and Expectations 273
13.3 Reliability Requirements 275
13.4 Solution Modeling and Analysis 279
13.5 Element Reliability Diligence 285
13.6 Solution Testing and Validation 285
13.7 Track and Analyze Field Performance 288
13.8 Other Solution Reliability Diligence Topics 292
14 SUMMARY 296
14.1 Service Reliability and Service Availability 297
14.2 Failure Accountability and Cloud Computing 299
14.3 Factoring Service Downtime 301
14.4 Service Availability Measurement Points 303
14.5 Cloud Capacity and Elasticity Considerations 306
14.6 Maximizing Service Availability 306
14.7 Reliability Diligence 309
14.8 Concluding Remarks 310
Abbreviations 311
References 314
About the Authors 318
Index 319
ERIC BAUER is a reliability engineering manager in theSoftware, Solutions and Services Group of Alcatel-Lucent. Theholder of more than a dozen U.S. patents, he is the author ofDesign for Reliability: Information and Computer-Based Systems,Beyond Redundancy: How Geographic Redundancy Can Improve ServiceAvailability and Reliability of Computer-Based Systems, andPractical System Reliability, also available from Wiley-IEEEPress. RANDEE ADAMS is a consulting member of technical staff inthe Software, Solutions and Services Group of Alcatel-Lucent andthe coauthor of Beyond Redundancy: How Geographic Redundancy CanImprove Service Availability and Reliability of Computer-BasedSystems.
For sure, specialists responsible for recommending,providing, or managing cloud platforms for either private or publiccloud will profit with having this work on their shelf. I wouldalso like to highly recommend this position for people new to theconsidered concepts of cloud computing or computer systemsreliability as it provides an excellent background for the bothareas. (IEEE Communications Magazine, 1 October2013) Therefore, it will probably only be of real interest tothose who are directly involved in improving or implementing theirown systems in a cloud platform. (ComputingReviews, 30 November 2012)
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