Dependable Computing: Design and Assessment(IEEE Press)

Dependable Computing Covering dependability from software and hardware perspectives Dependable Computing: Design and Assessment looks at both the software and hardware aspects of dependability. This book: Provides an in-depth examination of dependability/fault tolerance topics Describes dependability taxonomy, and briefly contrasts classical techniques with their modern counterparts or extensions Walks up the system stack from the hardware logic via operating systems up to software applications with respect to how they are hardened for dependability Describes the use of measurement-based analysis of computing systems Illustrates technology through real-life applications Discusses security attacks and unique dependability requirements for emerging applications, e.g., smart electric power grids and cloud computing Finally, using critical societal applications such as autonomous vehicles, large-scale clouds, and engineering solutions for healthcare, the book illustrates the emerging challenges faced in making artificial intelligence (AI) and its applications dependable and trustworthy. This book is suitable for those studying in the fields of computer engineering and computer science. Professionals who are working within the new reality to ensure dependable computing will find helpful information to support their efforts. With the support of practical case studies and use cases from both academia and real-world deployments, the book provides a journey of developments that include the impact of artificial intelligence and machine learning on this ever-growing field. This book offers a single compendium that spans the myriad areas in which dependability has been applied, providing theoretical concepts and applied knowledge with content that will excite a beginner, and rigor that will satisfy an expert. Accompanying the book is an online repository of problem sets and solutions, as well as slides for instructors, that span the chapters of the book.

Table of Contents:
About the Authors xxiii Preface xxv Acknowledgments xxvii About the Companion Website xxix 1 Dependability Concepts and Taxonomy 1 1.1 Introduction 1 1.2 Placing Classical Dependability Techniques in Perspective 2 1.3 Taxonomy of Dependable Computing 4 1.3.1 Faults, Errors, and Failures 5 1.4 Fault Classes 6 1.5 The Fault Cycle and Dependability Measures 6 1.6 Fault and Error Classification 7 1.7 Mean Time Between Failures 11 1.8 User- perceived System Dependability 13 1.9 Technology Trends and Failure Behavior 14 1.10 Issues at the Hardware Level 15 1.11 Issues at the Platform Level 17 1.12 What is Unique About this Book? 18 1.13 Overview of the Book 19 References 20 2 Classical Dependability Techniques and Modern Computing Systems: Where and How Do They Meet? 25 2.1 Illustrative Case Studies of Design for Dependability 25 2.2 Cloud Computing: A Rapidly Expanding Computing Paradigm 31 2.3 New Application Domains 37 2.4 Insights 52 References 52 3 Hardware Error Detection and Recovery Through Hardware- Implemented Techniques 57 3.1 Introduction 57 3.2 Redundancy Techniques 58 3.3 Watchdog Timers 67 3.4 Information Redundancy 69 3.5 Capability and Consistency Checking 93 3.6 Insights 93 References 96 4 Processor Level Error Detection and Recovery 101 4.1 Introduction 101 4.2 Logic- level Techniques 104 4.3 Error Protection in the Processors 115 4.4 Academic Research on Hardware- level Error Protection 122 4.5 Insights 134 References 137 5 Hardware Error Detection Through Software- Implemented Techniques 141 5.1 Introduction 141 5.2 Duplication- based Software Detection Techniques 142 5.3 Control- Flow Checking 146 5.4 Heartbeats 166 5.5 Assertions 173 5.6 Insights 174 References 175 6 Software Error Detection and Recovery Through Software Analysis 179 6.1 Introduction 179 6.2 Diverse Programming 183 6.3 Static Analysis Techniques 194 6.4 Error Detection Based on Dynamic Program Analysis 217 6.5 Processor- Level Selective Replication 233 6.6 Runtime Checking for Residual Software Bugs 239 6.7 Data Audit 242 6.8 Application of Data Audit Techniques 246 6.9 Insights 252 References 253 7 Measurement- based Analysis of System Software: Operating System Failure Behavior 261 7.1 Introduction 261 7.2 MVS (Multiple Virtual Storage) 262 7.3 Experimental Analysis of OS Dependability 273 7.4 Behavior of the Linux Operating System in the Presence of Errors 275 7.5 Evaluation of Process Pairs in Tandem GUARDIAN 295 7.6 Benchmarking Multiple Operating Systems: A Case Study Using Linux on Pentium, Solaris on SPARC, and AIX on POWER 308 7.7 Dependability Overview of the Cisco Nexus Operating System 326 7.8 Evaluating Operating Systems: Related Studies 330 7.9 Insights 331 References 332 8 Reliable Networked and Distributed Systems 337 8.1 Introduction 337 8.2 System Model 339 8.3 Failure Models 340 8.4 Agreement Protocols 342 8.5 Reliable Broadcast 346 8.6 Reliable Group Communication 351 8.7 Replication 358 8.8 Replication of Multithreaded Applications 370 8.9 Atomic Commit 396 8.10 Opportunities and Challenges in Resource- Disaggregated Cloud Data Centers 400 References 405 9 Checkpointing and Rollback Error Recovery 413 9.1 Introduction 413 9.2 Hardware- Implemented Cache- Based Schemes Checkpointing 415 9.3 Memory- Based Schemes 421 9.4 Operating- System- Level Checkpointing 424 9.5 Compiler- Assisted Checkpointing 432 9.6 Error Detection and Recovery in Distributed Systems 438 9.7 Checkpointing Latency Modeling 451 9.8 Checkpointing in Main Memory Database Systems (MMDB) 455 9.9 Checkpointing in Distributed Database Systems 463 9.10 Multithreaded Checkpointing 468 References 470 10 Checkpointing Large- Scale Systems 475 10.1 Introduction 475 10.2 Checkpointing Techniques 476 10.3 Checkpointing in Selected Existing Systems 484 10.4 Modeling- Coordinated Checkpointing for Large- Scale Supercomputers 492 10.5 Checkpointing in Large- Scale Systems: A Simulation Study 502 10.6 Cooperative Checkpointing 506 References 508 11 Internals of Fault Injection Techniques 511 11.1 Introduction 511 11.2 Historical View of Software Fault Injection 513 11.3 Fault Model Attributes 517 11.4 Compile- Time Fault Injection 517 11.5 Runtime Fault Injection 521 11.6 Simulation- Based Fault Injection 529 11.7 Dependability Benchmark Attributes 530 11.8 Architecture of a Fault Injection Environment: NFTAPE Fault/Error Injection Framework Configured to Evaluate Linux OS 531 11.9 ML- Based Fault Injection: Evaluating Modern Autonomous Vehicles 547 11.10 Insights and Concluding Remarks 574 References 574 12 Measurement- Based Analysis of Large- Scale Clusters: Methodology 585 12.1 Introduction 585 12.2 Related Research 587 12.3 Steps in Field Failure Data Analysis 594 12.4 Failure Event Monitoring and Logging 597 12.5 Data Processing 608 12.6 Data Analysis 622 12.7 Estimation of Empirical Distributions 634 12.8 Dependency Analysis 641 References 651 13 Measurement- Based Analysis of Large Systems: Case Studies 667 13.1 Introduction 667 13.2 Case Study I: Failure Characterization of a Production Software- as- a- Service Cloud Platform 667 13.3 Case Study II: Analysis of Blue Waters System Failures 686 13.4 Case Study III: Autonomous Vehicles: Analysis of Human- Generated Data 710 References 737 14 The Future: Dependable and Trustworthy AI Systems 745 14.1 Introduction 745 14.2 Building Trustworthy AI Systems 748 14.3 Offline Identification of Deficiencies 753 14.4 Online Detection and Mitigation 769 14.5 Trust Model Formulation 772 14.6 Modeling the Trustworthiness of Critical Applications 775 14.7 Conclusion: How Can We Make AI Systems Trustworthy? 786 References 788 Index 797